Machines doi: 10.3390/machines14080937

Authors: Nadia Akkari Malika Ikhlef Tarek Berghout Kamel Srairi Abderazek Hammoudi Aissa Laouissi

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m2) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications.

Machines doi: 10.3390/machines14080936

Authors: Amir Mahdi Amani Sajjad Amani AmirHossein MajidiRad

Mobile-robot navigation policies have typically assumed a fixed sensing input and robot platform. In this work, we investigate a teacher–student policy where teachers learn continuous velocity commands from LiDAR-based Twin Delayed Deep Deterministic Policy Gradient, and the student navigates using either LiDAR or camera observations. The student has two modalities with separate feature extractors. The extracted features and robot-state variables are fed into a shared encoder and actor, which map them to linear and angular velocity commands. The generalized two-platform configuration includes a Robot-ID token of scalar type. Training and evaluation were performed in ROS-Gazebo using simulated Pioneer 3-DX and TurtleBot3 Waffle robots. Robot-ID conditioning increased generalized LiDAR success to 90.4% on Pioneer 3-DX and to 91.2% on TurtleBot3 Waffle, up from 71.8% and 79.5%, and camera-based success was 87.3% and 84.0%, respectively. Across four controlled LiDAR-to-vision handover conditions, we achieved an overall success rate of 88.8-90.8%. Overall, 85.2-90.1% of the episodes that were still active at the planned switch were completed without resetting or retraining the policy. The results demonstrate that the conditioning on robot identity is very helpful for cross-platform LiDAR performance and that the shared student policy is able to continue navigation after external scheduling of the active sensing branch. The results provide a controlled, simulation-based feasibility assessment of the proposed policy architecture and establish the basis for subsequent physical robot validation.

Machines doi: 10.3390/machines14080935

Authors: Zhen Wang Shukai Li Xichu Wei Wenguang Fu

Variable-speed operation of scroll compressors is a prevailing trend for energy saving in refrigeration systems; however, complex electromagnetic excitation induces prominent vibration and noise, yet its dominant timing, spatial distribution, and action mechanism remain unclear. An electromagnetic–structural–acoustic sequential coupling model of a scroll compressor is established and validated at three speeds (3600–6600 rpm), and a dominance identification method integrating harmonic–modal matching, variational mode decomposition, and electromagnetic correlation identification is proposed. Predicted frequencies agree well with experiments; even-order harmonics migrate linearly with speed, with harmonic–modal matching exceeding 80% at low and medium speeds. At 5400 rpm, the 24th-order harmonic (2160 Hz) coincides with mode 2 (2162 Hz), causing resonance and a threefold amplitude increase. At low and medium speeds, vibration dominance indices range between 0.68 and 0.75, while noise dominance indices decrease from 0.55 to 0.48, dropping to 0.35 and 0.28 at 6600 rpm, indicating noise source transition. Vibration at S1 through S4 shows spatial variation, and far-field noise at F1 through F4 is non-uniform. These findings clarify how electromagnetic excitation dominates the vibration and noise of scroll compressors, providing a theoretical basis for speed-segmented and zone-specific noise source identification and control.

Machines doi: 10.3390/machines14080934

Authors: Yuxuan Wang Jinying Huang Hantao Liu Siyuan Liu Zhenfang Fan Yaxu Niu

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation frameworks and insufficient discriminative capability of feature indicators (FIs). To address these limitations, this paper proposes a new NFD method based on an adaptive multi-level spectra segmentation strategy. Firstly, using power spectral density (PSD) as the analysis basis, an iterative framework is constructed to obtain multi-level spectral trend lines (STLs), which achieves multi-perspective characterization of spectral features. Secondly, the local minimum points of the STLs are used as the segmentation boundaries to extract the demodulation frequency band. Subsequently, a robust blind feature indicator, synergistic characterization criterion (SCC), is proposed, which can simultaneously fully evaluate periodicity and impulsiveness, guiding the selection of the optimal demodulation frequency band (ODFB). Finally, based on the enhanced demodulation spectrum, power exponent transformation is introduced to construct a generalized spectral family, and the adaptive determination of the optimal transformation parameter is guided by frequency-domain signal-to-noise ratio (FDSNR), thereby obtaining the generalized enhanced demodulation spectrum (GEDS). Validation experiments on laboratory and public datasets demonstrate that the proposed method outperforms Fast Kurtogram, Autogram, and CFFsgram, with average improvements of 63.86% and 89.06% in mean-peak ratio (MPR) and fault feature coefficient (FFC), respectively, and provides a new perspective for NFD and expands its application potential in bearing fault diagnosis and condition monitoring.

Machines doi: 10.3390/machines14080933

Authors: Junjie Zhu Yang Ding Hui Li Bo Wang Dongbing Su Yonggang Xu

To address the difficulty of extracting weak fault features of rolling bearings in wind turbines under strong background noise, a fault feature extraction method based on the collaborative filtering correlation spectrum, named CESIgram, is proposed. The collaborative filtering correlation spectrum (CFCS) based on Block Matching 3D is designed to suppress random noise while preserving cyclostationary structures, resulting in a clearer cyclic spectral representation. A projection method along the cyclic frequency axis is proposed to obtain the carrier-based enhanced envelope spectrum. An integrated envelope spectrum index combining harmonic significance and periodic impact is proposed to quantify fault feature enrichment in different enhanced envelope spectra. The method works in three stages: spectral representation via Fast-SC, reformulation of the spectral correlation via CFCS, and adaptive band selection via CESI. The method successfully extracted fault characteristic frequencies and their harmonics in simulation and experimental signals under various strong noise conditions, while Fast Kurtogram, Autogram, Infogram, and Fast Entrogram failed to detect any fault-related peaks. Comparative analysis shows that the proposed method has significant advantages in noise suppression and fault feature extraction. The effectiveness is verified using simulation and experimental signals of rolling bearing faults in wind power equipment.

Machines doi: 10.3390/machines14080932

Authors: Nazmi Kaplan Alper Kadir Tanyıldızı

This paper presents the design, kinematic modeling, and dynamic simulation of a novel conical-slider-based transformable wheel with five deployable wheel-leg elements for mobile robotic systems. The proposed wheel can operate in a closed-wheel configuration for regular terrain and in an open wheel-leg configuration for enhanced interaction with rough terrain and obstacle profiles. The transformation motion is generated through a central linear actuation input transmitted by a conical slider mechanism integrated into the wheel hub. A CAD-supported mechanical design was developed to examine the geometric feasibility of the proposed wheel structure and to verify the radial deployment motion of the wheel-leg elements. The kinematic formulation was revised in a compact indexed form by consistently considering the angular offsets of all five wheel-leg elements. In addition, a dynamic model including the six-wheel vehicle body, suspension elements, wheel–ground contact, wheel-leg–ground contact, and wheel driving inputs was formulated. A unilateral contact model was used to represent contact, loss of contact, and re-contact events while preventing non-physical tensile normal forces. The proposed wheel concept was evaluated using a MATLAB-based representative mixed-terrain simulation scenario that combines rough-terrain locomotion and traversal of a 0.35 m single obstacle. The simulation results show that the fully deployed wheel-leg configuration successfully traverses the tested 0.35 m obstacle, whereas the closed-wheel configuration fails under the same terrain condition. Because the conical slider is continuously adjustable, an intermediate deployment state was also evaluated: it traverses a 0.30 m obstacle that the closed configuration cannot, yet fails against the 0.35 m obstacle, so that the traversal threshold varies monotonically with the deployment stroke. The comparison demonstrates that the deployed wheel-leg elements improve obstacle traversal capability by increasing the effective contact geometry and providing additional interaction with the obstacle surface. The results indicate that the proposed conical-slider-based transformable wheel has the potential to improve the terrain adaptability and obstacle traversal performance of six-wheel mobile robotic systems. Since the present study is limited to CAD-supported design verification and MATLAB-based dynamic simulation, future work will focus on prototype manufacturing, actuator design, structural analysis, and experimental validation under real terrain conditions.

Machines doi: 10.3390/machines14080930

Authors: Abdul Moiz Awan Selahattin Çağlar Başlamışlı Mesut Kaya Emrecan Hatipoğlu Oğuzhan Bayram Rümeysa Gençsev Kutsihan Pehlivan Mustafa Göleç

Heavy-vehicle Anti-lock Braking System (ABS) controllers are commonly tested using simulations before vehicle testing, but pneumatic actuation and measurement limitations can affect the results in the field. This paper presents the complete design process of a pneumatic ABS controller. System identification tests, estimation experiments, and reverse engineering methods were used to develop the models used for testing, including a vehicle model, pneumatic circuit model, etc. Model-in-the-loop (MIL) and software-in-the-loop (SIL) simulations were used to test and validate the algorithm in the simulation environment. Hardware-in-the-loop (HIL) simulations were also used to test and tune the ABS algorithm using a physical pneumatic circuit before real vehicle testing. Lastly, the ABS controller was deployed to a custom ECU and tested on the real vehicle on dry and wet asphalt. The developed controller prevented sustained wheel lock and produced repeatable deceleration and stopping-distance results during dry- and wet-asphalt testing. Qualitative assessment by the test driver and engineering team also indicated ABS intervention and modulation characteristics broadly consistent with commercial alternatives.

Machines doi: 10.3390/machines14080931

Authors: Fan Wu Yufan Zheng Wenkang Zhang

Human–robot collaborative flexible job shop scheduling (HRC-FJSP) must coordinate heterogeneous capabilities, mode-dependent processing times, safety feasibility, and carbon constraints. The problem becomes harder when a collaboration mode that is attractive during planning becomes infeasible after a human enters the robot safety separation zone. Unlike conventional dynamic disturbances such as machine breakdown or order insertion, this event changes the feasible collaboration mode of the unfinished operation remainder rather than only delaying a resource or adding a job. This study formulates a carbon-aware dynamic HRC-FJSP and evaluates a carbon-aware multi-agent deep reinforcement learning scheduler (CA-MADRL) with local recovery after safety-proximity-induced collaboration disruption. The objective combines normalized makespan, carbon emission, and human workload imbalance with carbon accounting based on operation energy and time-varying grid carbon intensity. Across the benchmark cases, CA-MADRL obtains the best average global criterion (0.7235), wins nine of 12 cases, and achieves the lowest average carbon emissions among the compared policies (48.991 kg CO2e). Sensitivity analysis shows that stronger carbon preference reduces emissions but increases makespan and tardiness, while adaptive collaboration outperforms fixed human–robot, human-only, and robot-only regimes. The results indicate that dynamic mode adaptation and local rescheduling improve carbon-aware collaborative schedules under safety disruption.

Machines doi: 10.3390/machines14080929

Authors: Yifan Li Jingtao Cheng Yue Zhao Ping Song

Strong broadband noise can mask the weak impact responses produced by bearing faults, while wavelet reconstructions selected only by signal-domain scores may not provide useful inputs for diagnosis. To address this problem, this paper presents WPMSR-1D-ResNet, a training-aware wavelet-domain controlled-noise augmentation framework. PKPM-guided particle swarm optimization first generates scale-specific perturbation candidates in the DWT detail coefficients. Signal-fidelity constraints remove distorted reconstructions, and a proxy CNN with identity fallback selects a global candidate according to validation Macro-F1. The final 1D-ResNet is trained jointly with the measured waveform and the selected augmented view, whereas inference uses only the raw signal. Under one fixed data construction and a common fixed 20-epoch budget, WPMSR-1D-ResNet achieved 0.8311±0.0607 Macro-F1 at the predefined CWRU low-SNR endpoint and ranked third among eleven methods, placing it within the leading statistical group. Its paired mean exceeded raw-signal 1D-ResNet and per-slice PKPM replacement by 0.05582 and 0.05227, respectively, with gains in nine of ten paired computational seeds. Mixed-SNR training increased mean Macro-F1 across eight mismatched-noise conditions from 0.5463±0.1320 to 0.6105±0.1292. The method ranked second on PU and third on acquisition-held-out AT data; on AT, it reduced the normal-state false-alarm rate from 0.2854 to 0.1646 while maintaining 0.9708 fault sensitivity. Raw-only inference required 0.266 ms per slice. WPMSR-1D-ResNet, therefore, aligns wavelet augmentation with diagnostic performance, preserves useful waveform information, and removes wavelet reconstruction and PSO from the deployment path.

Machines doi: 10.3390/machines14080928

Authors: Lidong Sun Ye Wang Zheheng Fan Fuchun Sun

Long-horizon navigation requires a high-level policy to select locally reachable subgoals, yet a scalar task reward provides little information about how an unsuitable proposal should be changed. We introduce Simulated Corrective Subgoal Supervision for Hierarchical Reinforcement Learning (SCS-HRL), a two-level method in which a topology- and clearance-aware programmatic supervisor evaluates each proposed subgoal and returns both a scalar score and a continuous target in the same subgoal space. The score trains the high-level critic, and the target enters a masked regression term for the high-level actor. Primitive actions are always conditioned on the actor’s subgoal; the supervisor is inactive during learned-policy evaluation. In AntMaze, using 6000 training episodes, five seeds, and 100 deterministic evaluation episodes per seed, SCS-HRL attained an 88.4±7.8% final success rate (mean ± sample standard deviation; 95% Student-t confidence interval [78.7%,98.1%]). The matched scalar-only condition and HIRO attained 0% rates. Applying the same route rule directly to the SCS-HRL low-level controllers yielded 82.2±9.9% success; the paired difference favored the learned high-level policy by 6.2 percentage points (95% confidence interval [2.1,10.3], p=0.013). Across three matched seeds, nonzero corrective weights of 0.5, 1.0, and 2.0 remained stable, whereas 0.25 was seed-sensitive. Term-level ablations further show that the continuous target, rather than the exact scalar-shaping formula, was the principal additional signal. Separate fixed-policy tests obtained 0% success rates on two unseen maze layouts. These results indicate that continuous subgoal targets can encode task-specific route information in the source maze, while cross-layout transfer remains unresolved.

Machines doi: 10.3390/machines14080926

Authors: Haocong Xu Bingliang Ye Xuewen Huang Yaxin Yu Gaohong Yu Liang Sun

This study investigates the dynamic response of non-circular gear planetary trains in transplanting mechanisms, focusing on variable transmission effects. A time-varying mesh stiffness model was developed for non-circular gears using pitch curve parameters, incorporating pressure angle, contract ratio, and equivalent teeth number as dynamic variables. A dynamic model of the planetary gear train was established to analyze component vibration characteristics. Comparative analysis reveals that non-circular gears’ variable-speed transmission significantly amplifies gear train vibrations compared to that of circular gears. Structural angle effects were examined, demonstrating the structural angle’s critical role in modulating vibration energy distribution between sun and planet gears. Frequency-domain analysis identified optimal structural angle ranges that minimize resonance risks by controlling component center vibrations. This work clarifies the coupling mechanisms between geometric parameters and transmission characteristics in non-circular gear systems. A design criterion based on frequency–energy distribution is proposed to optimize high-speed transplanting mechanisms. These findings advance the understanding of vibration modulation in variable-ratio gear trains and provide theoretical guidance for enhancing operational stability in agricultural machinery.

Machines doi: 10.3390/machines14080927

Authors: Hanlin Lei Benjamin Chong Kang Li

Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete driving cycle, so that operational behaviour, not static metrics, determines selection. A fully documented post-evaluation criterion aggregates tracking, battery electrical stress, soft constraint violations and design overhead into one score normalised against an exact baseline anchor. Because one evaluation costs about 60 ms, the complete exact Pareto front of an electric transit bus case study is screened, not a sample. The static design cost proves almost uninformative regarding dynamic performance: the rank correlation between the two orderings is statistically indistinguishable from zero, the sets that they rank highest share no member, and the statically cheapest design falls far down the dynamic ranking, ending below the baseline. The cause is structural opposition on the pack voltage, which improves the dynamic performance but raises the static cost. The framework returns a leading design family that improves on the baseline overall, quantifies the battery stress that its leaner supercapacitor incurs, and shows the verdict to be robust to controller tuning but dependent on the duty and control strategy.

Machines doi: 10.3390/machines14080925

Authors: Chenfei Wen Siyuan Zhang Maksim A. Grigorev Ivan Kholodilin Victor Kushnarev Dmitry Khriukin Nikita Maksimov

End-effector obstacle-avoidance trajectory planning is essential for improving the autonomy, safety, and executability of industrial robotic manipulators in constrained workspaces. Conventional Rapidly Exploring Random Tree (RRT) planners provide effective exploration capability but often suffer from stochastic tree expansion, redundant trajectories, and insufficient directional guidance near obstacle regions, which limits planning efficiency and trajectory quality. This study proposes a clearance-field-guided RRT framework with behavior-cloning-assisted refinement for end-effector obstacle-avoidance trajectory planning of industrial robotic manipulators. The proposed method formulates the planning problem in Cartesian space based on an end-effector kinematic model and introduces local clearance-field guidance into the RRT sampling process. Candidate samples are evaluated by considering obstacle clearance, reference-line deviation, and goal distance, enabling the search tree to preferentially expand toward effective traversable regions while maintaining the exploration capability of conventional RRT. Behavior cloning is further introduced as an offline auxiliary strategy to investigate the influence of expert trajectories on local motion-direction learning and trajectory continuity. A Python–Unity joint simulation–verification framework and a physical manipulator experimental platform are established to evaluate the feasibility and practical executability of the generated trajectories. Python is used for offline trajectory generation, expert dataset construction, behavior-cloning training, and performance evaluation, while Unity is employed for three-dimensional manipulator modeling and trajectory reproduction. The experimental results demonstrate that the proposed Field-guided RRT achieves a better balance among path efficiency, planning time, obstacle-clearance maintenance, and trajectory execution capability compared with conventional RRT-based methods. The proposed framework provides an effective solution for collision-free end-effector trajectory planning in industrial applications such as assembly, welding, component placement, and robotic inspection.

Machines doi: 10.3390/machines14080924

Authors: Juncheng Zhu Zhan Gao Zhile Yang Yuanjun Guo

Robust three-finger grasping under physical-domain variation remains challenging because contact stability can change substantially with object mass, effective friction, and observation noise. This work develops U-GRA, a conservative offline-to-online residual adaptation framework for simulated three-finger grasping. U-GRA introduces a unified prior-preserving and critic-disagreement-regulated architecture that couples a frozen behavioral prior with a spectrally normalized and bounded residual stream, scalar Twin-Q reliability assessment, and critic-conditioned residual fusion. The framework first learns a nominal behavioral prior from successful demonstrations and then freezes it as a stable action anchor during online adaptation. Before execution, the twin critics evaluate a candidate action formed from the prior action and the bounded residual proposal, and their absolute scalar Q-value disagreement conditions a state-dependent gate that regulates residual-injection strength. Experiments are conducted in CoppeliaSim using an offline dataset of 40,000 successful demonstrations and online randomization of object mass, effective friction, and observation noise. Across three independent seeds, U-GRA achieves a mean success rate of 84.8±2.3%, a normalized return of 82.7±4.1, and a jitter value of 0.12±0.03. Relative to AWAC-Res, the strongest evaluated baseline, U-GRA improves mean success by 9.2 percentage points and reduces jitter by 57.1%. It also retains the highest mean success rate and normalized return over the unseen simulated high-mass–low-friction OOD region. These results provide simulation evidence that preserving a nominal behavioral prior while regulating bounded residual correction through critic disagreement improves three-finger grasping robustness under physical-domain variation.

Machines doi: 10.3390/machines14080923

Authors: Zeyu Sun Yiheng Ren Yiyong Jiang Ruochen Wang

Agricultural machinery is rapidly developing toward electrification, intelligence, and autonomy, and the electric drive chassis has become a key technology for improving power transmission performance, operational efficiency, and energy utilization. This paper presents a review of research on electric drive chassis for agricultural machinery, focusing on four major aspects: electric drive systems, anti-slip and stability control of electric drive chassis, autonomous navigation system control technologies, and energy management strategies. The electric drive system is reviewed from the perspectives of drive motor technologies and drive architectures. Chassis control technologies are mainly discussed in terms of longitudinal anti-slip control and lateral stability control under complex terrain conditions. Autonomous navigation systems are summarized with respect to multi-source environmental perception, path planning, and path tracking control. Energy management strategies are classified into rule-based, optimization-based, and learning-based approaches according to their control principles, and the characteristics and applicable scenarios of each approach are analyzed. On this basis, the collaborative relationships among drive architecture, chassis control, autonomous navigation, and energy management are further discussed. Finally, future research directions are proposed, including highly integrated electric drive systems, vehicle-level collaborative control, multi-source sensor fusion, hybrid model-driven and data-driven control, global energy optimization, and multi-machine cooperative operation. This review provides a reference for the design and development of intelligent electric drive chassis for agricultural machinery.

Machines doi: 10.3390/machines14080922

Authors: Xinyang Gu Zhong Tang Jianpeng Jing Jiahao Shen Lulu Yuan

Bolted connections in combine-harvester vibrating screens are affected by reciprocating screen motion, frame vibration, and intermittent impact, making weak local vibration components difficult to distinguish from background fluctuations. This study used particle swarm optimization to adaptively select the wavelet basis, decomposition level, and thresholding rule for vibration signal denoising. Triaxial acceleration signals were collected under tightened- and loosened-bolt conditions. Gaussian white noise with input signal-to-noise ratios from 0 to 14 dB was added to evaluate parameter selection under controlled noise levels. The selected wavelet basis and decomposition level varied with the input signal-to-noise ratio. Three or four decomposition levels were selected under stronger noise, whereas two levels were generally sufficient at higher signal-to-noise ratios. Soft thresholding was selected in all tested cases. The optimized method reduced random fluctuations while retaining the main waveform trend in simulated noisy signals. For measured loosened-bolt signals, it reduced background fluctuation and retained local waveform changes.

Machines doi: 10.3390/machines14080921

Authors: Mahmoud Fouad Mostafa Rizk Anoud Nagaf Mostafa Abdelmoez

Pressure losses in heating, ventilation, and air-conditioning (HVAC) duct elbows significantly increase fan power requirements and reduce overall system efficiency. This study presents a comprehensive computational fluid dynamics (CFD) investigation aimed at identifying effective strategies for reducing pressure losses in 90° HVAC duct elbows. The numerical methodology was first validated against published experimental measurements, demonstrating excellent agreement and providing confidence in the predictive capability of the CFD model. The validated model was then employed to evaluate the influence of duct geometry, inlet velocity, guide vane configuration, inter-vane spacing, perforated guide vanes, and duct material roughness on aerodynamic performance using the SST k–ω turbulence model. The results show that round elbows reduce pressure losses by approximately 50% compared with hydraulically equivalent rectangular elbows, highlighting the strong influence of duct geometry on flow separation. Among the flow-control strategies investigated, curved guide vanes produced the greatest improvement, with an optimized three-vane arrangement and a non-dimensional spacing of s/Dh≈0.15 (corresponding to 150 mm for the specific geometry tested) reducing pressure losses by approximately 31% relative to the baseline elbow without guide vanes. In contrast, the investigated perforated guide vane provided only marginal improvement, indicating that its geometry requires further optimization to minimize blockage and mixing losses. The material roughness study showed that smooth, rigid duct materials produced only minor differences in pressure loss, whereas flexible ducts generated noticeably higher losses because of their increased surface roughness. These findings demonstrate that optimizing elbow geometry and guide vane design is considerably more effective than modifying duct material or using the investigated perforated vane configuration. The study provides practical design recommendations for improving the aerodynamic performance and energy efficiency of HVAC duct systems.

Machines doi: 10.3390/machines14080920

Authors: Yue Feng Hongtao Wang

Distribution system restoration (DSR) has become increasingly challenging due to frequent topology changes and complex nodal interactions, especially in systems with a high penetration of distributed energy resources (DERs). Existing deep reinforcement learning (DRL) methods remain limited in representing inter-nodal relationships and incorporating domain prior knowledge. Therefore, this paper proposes a graph attention-based coupling-aware reinforcement learning method. From a non-Euclidean spatial perspective, the proposed method uses the distribution power transfer factor (DPTF) to quantify the strength of electrical coupling between nodes. The resulting coupling strengths are embedded as entries of the graph adjacency matrix, allowing the model to capture complex nodal interactions driven by power transfer. An aware graph attention network (AGAT) is further developed, where adjacency matrix with prior knowledge is introduced as a bias term in the attention coefficient calculation. This design guides GAT to generate differentiated node representations enriched with physical information. Based on the extracted graph features, proximal policy optimization (PPO) is employed to determine restoration decisions. Case studies on the IEEE 34-bus system demonstrate that the proposed method outperforms benchmark algorithms in training convergence, restored power, and online decision efficiency, enabling fast and effective distribution system restoration.

Machines doi: 10.3390/machines14080919

Authors: Hojin Jung

This paper proposes an integrated control allocation method for a vehicle equipped with all-wheel drive (AWD) and an electronic limited-slip differential (eLSD). Unlike those of electric drive systems, the admissible clutch inputs of AWD and eLSD systems vary with drivetrain structure, engine torque, and vehicle operating conditions. To address this issue, a weighted least squares (WLS) control allocation framework is designed to coordinate the transfer clutch and the left/right eLSD clutches while considering physically derived input constraints. The proposed controller is evaluated using a CarSim–Simulink AWD-eLSD vehicle model under acceleration during steady-state cornering and double-lane-change maneuvers on high- and medium-friction road surfaces. The simulation results show that the proposed method improves yaw-rate tracking while maintaining small sideslip angles and bounded rear-wheel slip ratios compared with uncontrolled and maximum-eLSD-input cases. In addition, the proposed control allocation method requires substantially lower computation time than an MPC-based approach, supporting its suitability for real-time AWD and eLSD control applications.

Machines doi: 10.3390/machines14080918

Authors: Penghui Wu Lingda Hu Lu Wang Yutong Zu Yin Qing Yuanbiao Hu

The bent sub is a main deflection component in the near-bit assembly of small-diameter push-the-bit guided coring tools, and its parameters affect build-up capability and local structural response. Existing studies mainly focus on conventional rotary steerable drilling systems, whereas slim-hole constraints, including narrow annular clearance and cross-sectional weakening induced by internal coring channels, remain insufficiently considered. To address this problem, a static bending model of the near-bit section was established based on Euler–Bernoulli beam theory. Channel-induced cross-sectional weakening was represented using the actual concentric annular geometry of the primary load-bearing outer tube, and the bent-sub initial curvature, dual push-the-bit loads, axial weight on bit, and borehole-wall contact and friction effects were incorporated. The build-up rate (BUR), maximum equivalent stress, and maximum curvature served as response indicators. A control-variable approach was used to analyze the bent-sub length Lb, bend angle γ, and distance from the bit Db. The results showed that Db had the strongest effect on BUR, and all parameters exhibited a trade-off between steering performance and structural safety. Increasing Lb from 0.30 m to 0.80 m reduced BUR from 12.65°/30 m to 9.79°/30 m, whereas increasing γ from 0.5° to 2.5° increased BUR from 4.12°/30 m to 10.70°/30 m. Considering structural constraints and normalized BUR retention, the recommended engineering ranges are Lb = 0.65–0.80 m, γ = 1.3°–1.9°, and Db = 0.50–0.70 m.

Machines doi: 10.3390/machines14080917

Authors: Gyan Wrat Mohit Bhola

This study presents a cost-effective approach for detecting internal leakage faults in hydraulic cylinders by leveraging features extracted from existing control signals, specifically the PID valve input. The key innovation lies in eliminating the need for additional sensors or hardware modifications, making the method suitable for low-cost real-time implementation. Several low-complexity, time-domain features were identified and extracted from the control signal, which reflect changes in system behavior due to internal leakage. These features are designed for edge computing platforms, enabling practical deployment in industrial environments. The proposed method is particularly applicable to systems with known loads and consistent duty cycles, such as hydraulic presses, where deviations in control signal behavior can reliably indicate leakage. However, limitations arise when applied to systems with stochastic or highly variable loading, such as mobile machinery, where external disturbances can obscure leakage effects. This approach enables early fault detection and condition monitoring in hydraulic systems without increasing system complexity or cost. It provides a foundation for predictive maintenance strategies in both stationary and mobile hydraulic equipment, contributing to improved reliability and reduced downtime.

Machines doi: 10.3390/machines14080916

Authors: Jingyu Yang Jikai Xu Li Peng Longfu Luo Wanting Li Hengrui Ma

Motor fault identification is often constrained by scarce labeled samples and the limited representation capability of a single time–frequency transform. Conventional CNN–Softmax models may also produce unstable decision boundaries under small-sample conditions. To address these issues, this paper proposes a motor fault identification method based on the fusion of fixed-resolution and multiscale time–frequency features. Each vibration segment is transformed into short-time Fourier transform (STFT) and synchrosqueezed wavelet transform (SWT) maps. Two parallel convolutional branches extract complementary features, which are fused by element-wise addition and classified using a radial basis function support vector machine. Experiments on the HUST motor multimodal fault dataset show that the proposed method achieves 100% accuracy under the conventional 70%/30% train–test split. When the training proportion is reduced to 20%, 15%, 10%, and 5%, the corresponding accuracies remain at 99.46%, 99.10%, 98.78%, and 96.77%, respectively. Across operating speeds of 5, 10, 20, and 30 Hz, the average accuracies reach 98.75% and 94.61% under the 20% and 5% training conditions. The model also maintains 100% accuracy at signal-to-noise ratios of 15 dB and above. These results demonstrate that complementary time–frequency feature fusion combined with maximum-margin classification improves identification accuracy and decision-boundary stability under limited training data.

Machines doi: 10.3390/machines14080915

Authors: Guolin Yang Min Zhou Yifan Cao Lehao Zhang Guofeng Ma

Carbon fiber reinforced plastic (CFRP) composites have been widely adopted in the aerospace industry due to their excellent mechanical and physical properties. However, their anisotropy and weak interlaminar bonding make them prone to defects such as delamination and fiber pull-out during conventional drilling (CD). Longitudinal-torsional coupled ultrasonic vibration-assisted drilling (LTC-UAD) integrates axial and circumferential vibrations to suppress hole defects and is considered a promising machining method for improving the quality of holes drilled in CFRP. Based on kinematic analysis, a model for the working rake angle of the main cutting edge is established to obtain the variation law of the maximum working rake angle along the cutting edge. Compared with CD and longitudinal ultrasonic vibration-assisted drilling (L-UAD), LTC-UAD significantly increases and homogenizes the maximum working rake angle of the main cutting edge, which helps optimize its cutting performance. A three-dimensional finite element model of CFRP is constructed to analyze the dynamic fiber removal process under typical fiber orientations. Finally, drilling experiments are performed to observe the hole wall micro-morphology at various fiber angles. The simulation results indicate that ultrasonic vibration causes periodic changes in the fiber cutting angle, subjecting the fibers to a directional shear state and making them more prone to shear fracture. Two-dimensional ultrasonic vibration cutting enhances the directional shear effect, promotes fiber fracture, accelerates chip removal, and improves the quality of the machined surface. Experimental observations confirm LTC-UAD alleviates fiber crushing, bare fibers, and surface cavities with uniform resin coverage. Furthermore, ultrasonic vibration suppresses thrust force. L-UAD and LTC-UAD yield 10.6% and 17.1% reductions via periodic cutting depth variation and facilitated carbon fiber shear fracture.

Machines doi: 10.3390/machines14080914

Authors: Yue Gao Bingjie Ma Hangfeng Mo Tao Tao Zhinong Jiang Zhiwei Mao

When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or missed detections. Meanwhile, fault samples are usually limited in practical applications. To address these problems, this study proposes an anomaly detection method based on multi-feature relational modeling and conditional-memory augmentation. The method performs the cycle-wise alignment of multi-point vibration signals according to the firing phase of each cylinder. It integrates local waveform morphology, impact energy, and energy-centroid information in the non-uniform angular domain to construct a raw–relative dual relational representation. It further uses speed conditions to modulate latent features and employs a sparse normal memory to constrain reconstruction sources, enabling the model to learn normal relational patterns under different operating conditions using only normal samples. Tests involving misfire, intake-valve clearance anomaly, and exhaust-valve clearance anomaly were conducted on a TBD234V12 diesel-engine test bench. The proposed method achieved an accuracy, true positive rate (TPR), F1-score, and area under the receiver operating characteristic curve (AUROC) of 97.44%, 98.98%, 98.30%, and 98.88%, respectively, with a false-positive rate (FPR) of 7.21% under the sample-level alarm definition. The results show that the method reduces the interference of operating-condition-induced normal-pattern drift with anomaly determination and improves the accuracy of fault warning within the range of the operating conditions covered in this study.

Machines doi: 10.3390/machines14080913

Authors: Jisheng Xing Naixing Li Xin Fang Zhankun Wang Feng Zhang Luyao Cui Jing Bai Yu Xu

Mechanical speed sensors increase cost and may reduce the reliability of permanent magnet synchronous motor (PMSM) drives under harsh conditions. This paper proposes a validation-protected physics-consistent probabilistic neural estimator for sensorless PMSM speed estimation using only online-deployable signals: the previous estimated speed, measured d/q-axis currents, and commanded d/q-axis voltages. A multi-output probabilistic network predicts the speed distribution and auxiliary residual-compensation variables. Mechanical consistency, electrical consistency, and regularization losses are imposed during training, while a validation-protected rule selects, for each random seed, the checkpoint with the lower validation RMSE from the paired baseline and physics-trained candidates. Experiments use a frozen multi-seed protocol covering locked holdout evaluation, independent comparison, and disturbance tests. Across Datasets 8–11, the frozen predictive distributions yielded Gaussian NLL values from 3.190 to 3.239, 100% empirical coverage of the nominal 95% prediction intervals, and mean interval widths of approximately 35.9 rad/s, indicating conservative rather than well-calibrated uncertainty. On locked Dataset 7, Physics-safe reduced the mean RMSE from 3.415 to 3.277 and the inter-seed standard deviation from 0.290 to 0.052. Results on Datasets 8–11 show that the method is not universally mean-error optimal; its recurring advantage is lower inter-seed variability and more reproducible training outcomes. A local sensitivity analysis on Dataset 4 confirmed seed- and loss-weight-dependent physics-training outcomes, supporting the need for validation protection without implying globally optimal loss weights. On the specified desktop CPU using ONNX Runtime, the complete recursive estimator step required 40.154 microseconds, below the adopted 100-microsecond sampling interval, supporting estimator-level computational feasibility.

Machines doi: 10.3390/machines14080912

Authors: Chengpeng Li Yanru Bi Gang Wang Min Zhang

Uneven straw conveying and unstable throwing can reduce the operational performance of rice combine harvesters. To address these problems, an integrated straw conveying and throwing device combining guided conveying with pneumatic throwing was developed. The brachistochrone principle was introduced into the curved-surface design of the diversion plate as a geometry-guided approach to provide a continuous transition between the straw-falling region and the conveying inlet. Based on the motion characteristics of straw in the diversion and throwing regions, a coordinated feeding–acceleration–throwing process was established. The effects of diversion plate angle, blade rotational speed, and blade installation angle on throwing distance and distribution stability were investigated. A computational fluid dynamics model based on the mixture multiphase approach was used to characterize the macroscopic gas–solid flow field and compare airflow organization under different blade installation angles. A Box–Behnken response surface design was subsequently employed to establish regression models for throwing distance and the coefficient of variation in straw distribution, followed by multi-response numerical optimization. The optimal parameter combination consisted of a blade rotational speed of 2500 r/min, a diversion plate angle of 1.25 rad, and a backward blade installation angle of 15°. Under these conditions, the predicted throwing distance and coefficient of variation were 7.89 m and 14.6%, respectively. Validation tests produced throwing distances of 6.94–8.21 m and coefficients of variation of approximately 13%, showing good agreement with the predicted performance. The developed device and optimization results provide a basis for improving the conveying continuity and throwing uniformity of straw-handling systems in combine harvesters.

Machines doi: 10.3390/machines14080911

Authors: Paul-Adrian Pascu Laurentia Andrei

Current design approaches for non-circular gears are generally restricted to predefined pitch curve geometries, lacking a unified methodology for systematically exploring wider families of admissible curves. To overcome this limitation, the paper proposes a generalized parametric design methodology capable of generating and evaluating an entire family of feasible pitch curves from a single analytical formulation—the Gielis’ superformula. Numerical analysis and CAD-based implementation are employed to identify a sub-family of generalized ellipses within the infinite family of Gielis curves that meet the geometric requirements for non-circular gear centrodes, namely closed and periodic profiles, smooth curvature, the absence of angular discontinuities and undercutting during tooth generation. The proposed methodology integrates the Gielis curves into gear design by (i) introducing gear-specific parameters, (ii) generating the conjugate pitch curve through the rolling without-slip equations, (iii) determining the center distance, (iv) determining the tooth number relation associated with the revolution ratio, (v) considering the curvature-based undercutting criterion and (vi) validating the resulting gears through CAD-based tooth generation simulations. This approach extends the geometric design space while providing a unified framework for the non-circular gears synthesis. Appropriate combinations of the Gielis parameters allow the amplitude, frequency and waveform of the transmission ratio function to be tailored to the kinematic requirements of variable-speed applications. Rather than identifying universal optimal values, the proposed methodology provides a framework for systematically evaluating Gielis parameter combinations against the geometric and gear-design requirements.

Machines doi: 10.3390/machines14080910

Authors: Xiao Yang Xiaoping Xie Nanquan Jiang Pengchuan Wang Xuan Zhao

This paper investigates the gear whine problem in a high-speed heavy-load reducer. A rigid-flexible coupled multibody dynamic model of the reducer is established using Romax. Transmission error, unit load, tooth root stress, and contact stress are used as optimization objectives. A comprehensive gear micro-modification method including lead crowning, lead slope, involute crowning, and involute slope is proposed, and a genetic algorithm is employed for optimization. The peak-to-peak transmission error [TE(p-p)] decreased from 0.27 μm to 0.19 μm for the first-stage gear pair and from 2.67 μm to 0.96 μm for the second-stage gear pair, corresponding to reductions of 29.63% and 64.04%, respectively. The maximum contact stresses decreased from 507 MPa to 488 MPa (3.75%) and from 627 MPa to 618 MPa (1.44%) for the first- and second-stage gear pairs, respectively. The radiated noise of the gearbox was reduced by about 10 dB on average. The proposed method provides a reference for the microgeometry design of high-speed heavy-load reducers.

Machines doi: 10.3390/machines14080909

Authors: Didem Tekgun Burak Tekgun

Manufacturing tolerances and winding-layout variations can introduce phase-to-phase mismatches in stator resistance and leakage inductance. Under such unbalanced phase impedances, conventional field-oriented control (FOC), typically designed under balanced-parameter assumptions, may produce unequal phase currents, distorted airgap MMF, reduced efficiency, increased torque ripple, and undesired vibro-acoustic behavior. This paper investigates an open-end winding (OEW) induction machine (IM) drive, in which each phase is independently driven by an H-bridge inverter fed by the same DC source. To mitigate phase–current imbalance without parameter estimation, an RMS-based phase–current-balancing controller is proposed. The controller continuously calculates the RMS value of each phase current and adaptively scales the corresponding reference-phase voltage in a low-bandwidth outer loop, while preserving the classical FOC structure. The balancing law is derived directly from the phase-impedance imbalance model; convergence of the three coupled per-phase loops is proven via a Lyapunov argument, and stability of the cascaded structure is established through an analytical bandwidth-separation analysis shown to be robust to ±30% machine-parameter variation and across the 500–1500 rev/min speed range. Simulation and experimental results across multiple operating points demonstrate effective phase–current equalization.

Machines doi: 10.3390/machines14080907

Authors: Shaojie Luo Le Wang Jianxiang Xi Mingxing Qin Liyu Song

This paper develops a distributed cooperative guidance law for the spatiotemporal cooperative arrival of underactuated flight vehicles with uncontrollable axial acceleration under obstacle avoidance constraints. First, a singularity-free obstacle avoidance guidance law is proposed based on a linear projection function to avoid singularity-induced surges in acceleration commands. The proposed law has a simple structure and bounded magnitude, and it ensures that the flight vehicles safely avoid obstacle regions. Then, error dynamics theory is adopted to design a cooperative guidance law for arrival angle control and arrival time synchronization, which guarantees that the coordination errors of the flight vehicles converge to zero before reaching the target. Moreover, a buffer zone is constructed around each obstacle, which provides a distance-dependent transition region. Accordingly, a continuous and smooth weighting function is designed to shift the guidance priority from cooperative guidance to obstacle avoidance, thereby avoiding abrupt jumps in acceleration commands during task switching. Finally, the effectiveness of the proposed guidance law is verified through numerical simulations in typical scenarios and Monte Carlo experiments.

Machines doi: 10.3390/machines14080908

Authors: Jingming Gai Wei Li Bo Wang Xiangchao Shi

As global oil and gas exploration advances into deep reservoirs, reaming-while-drilling (RWD) tools (hereinafter referred to as the reamer) are widely used to enlarge wellbores for unconventional well structures, prevent stuck pipe caused by formation shrinkage, and improve cementing quality. The flow distribution and pressure loss of reamer directly determine their operational performance, and thus, affect the success rate of reaming operations and construction quality. However, limited by intellectual property protection of core technologies and commercial barriers, no general hydraulic calculation method for reamers is publicly available. This paper presents theoretical calculations of flow distribution and pressure loss for reamers and verifies their accuracy against numerical simulations and lab tests. The results show that at a field flow rate of 40 L/s, the relative error between theoretical and experimental pressure loss is only 2.93%. Flow distribution between the bit and reamer depends solely on equivalent nozzle diameter, which dominates bottom hole assembly (BHA) pressure loss and directly governs blades pushing force. Extra flow outlets during activation cause negligible pressure loss reduction, yet pressure change at pin failure remains the key status-switching criterion. The results of this paper can serve as a theoretical reference for the research and development and field deployment of reamer.

Machines doi: 10.3390/machines14080906

Authors: Mohamed Krichi Mhamed Fannakh Abdullah Noman Tarik Raffak Sulaiman Almutairi Abdullah Alharbi

Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are seldom reported. This paper addresses both questions on an EMM-class test bench built around a 36 V, 250 W in-wheel BLDC motor. A proportional-integral (PI) regulator and a first-order Takagi-Sugeno (TS) fuzzy regulator are specified in Simulink, auto-coded to ANSI-C by Embedded Coder, and deployed unchanged on an STM32F446RE target driving a custom three-phase inverter through six-step Hall commutation. Over a six-step, 180 s duty cycle reaching 21.1 km/h, the two regulators are shown to occupy opposite ends of the speed-versus-damping trade-off. On the 30 to 100 RPM ascending step under load, the PI reaches the set-point in 0.4±0.1 s with 21.6% overshoot and the TS in 2.7±0.1 s with 1.5% overshoot, both quoted at the resolution of the 10 Hz acquisition, and over the complete duty cycle, a window that also contains segments on which neither regulator has control authority, the TS lowers the tracking RMSE by 9.4%. A structural analysis of the deployed firmware excludes the realisation form as the cause. The positional and incremental forms are algebraically equivalent while the command is unsaturated, which is the regime of the step above. Under saturation, the incremental accumulator of the TS is not clamped and winds up exactly as the positional PI integrator does. The two loops are also shown to share the same unfiltered speed feedback and the same command saturation limits. The difference is traced instead to the effective gains realised by the seven consequents. Far from the set-point, the TS applies an integral gain three to twelve times weaker than the PI for a comparable proportional gain. A fixed-gain PI in that range is predicted to reproduce the response for one eighth of the Flash. The embedded cost of both regulators is then quantified on the target from the linker map, the fuzzy controller occupying 2325 Bytes of Flash against 266 Bytes for the PI, a factor of 8.7, and 200 Bytes of stack against 32 Bytes, a factor of 6.3, rising to 248 Bytes against 32 Bytes when the complete call tree is counted, for 0.45% of the available Flash. The complete platform, comprising the inverter, the Hall front end, the auto-generated firmware, and a Python supervisory interface, is described together with its deployed timing, PWM, and saturation parameters.

Machines doi: 10.3390/machines14080905

Authors: Han Peng Budi Peng Linjian Shangguan Mingxuan Zhang Minzhang Zhao Lei Liu Zihao Qin Zihao Meng Yihao Zhang Bingli Huang

With the advancement of industrial IoT and artificial intelligence technologies, bearing maintenance is gradually evolving toward predictive maintenance. For large bearings, the internal grease must be replaced promptly once it has deteriorated. As the core lubrication component of such bearings, the suction and discharge device directly determines the efficiency of grease discharge and the operational stability of the bearing. To address the issue of insufficient intake capacity in existing units, this study employs the Herschel–Bulkley non-Newtonian fluid model to analyze intake characteristics and conduct multi-parameter co-optimization, revealing the underlying mechanisms by which vacuum level, grease temperature, and the chamfer structure of the grease inlet pipe influence suction performance. Based on the yield stress and shear thinning characteristics of the grease, the flow equation for the inlet section was derived, and the analytical and CFD results showed consistent trends. With the volumetric flow rate in the inlet section as the optimization objective, a multi-parameter co-optimization of the vacuum level, temperature, and chamfer radius was conducted through orthogonal experiments. The results show that under the optimal parameter combination, the inlet volumetric flow rate was significantly increased, and grease supply stability was markedly improved. The research findings provide a theoretical basis and engineering reference for the design optimization of the suction and discharge device for wind turbine bearings.

Machines doi: 10.3390/machines14080904

Authors: Ji Li Junjie Dan Yaling Tian Min Ling Heng Zhao Weiqiang Mo Yi Luo Yaoming Zhou

In aircraft assembly, riveting is a critical joining method that directly determines structural integrity, fatigue life, and overall airframe reliability. With the increasing adoption of human–machine collaborative systems for complex assembly tasks, the rational design of riveting process parameters has become essential for ensuring consistent assembly quality. However, traditional experimental parameter optimization is time-consuming and costly, and lacks generalizability across varying working conditions. To address this challenge, this paper proposes a simulation-based design method for rapidly constructing process parameter schemes in human–machine collaborative aircraft assembly riveting. A theoretical dynamic model of the pneumatic reciprocating riveting gun is established to derive the relationship between input air pressure and piston impact velocity, providing physically grounded loading conditions for numerical simulation. A sequentially coupled numerical simulation method is developed using Ansys LS-DYNA and its Restart function to accurately model the entire multiple reciprocating impact forming process, which incorporating preloading analysis to reflect actual clamping conditions and reset analysis with applied damping to eliminate post-impact oscillations. Taking the riveting assembly of Aluminum (AL) 2024T351 rivets and AL 7039 aluminum sheets as a case study, the simulation successfully reproduces the rivet forming evolution over twelve consecutive impacts, revealing a two-stage deformation mechanism consisting of elastic springback and superimposed elastic-plastic deformation. Experimental verification on a self-built human–machine collaborative riveting platform demonstrates excellent agreement with simulation results in impact counts and upset head height. The proposed method provides a reliable, efficient, and low-cost approach for assembly process parameter calibration, offering direct theoretical support for assembly quality control, process robustness, and reliability assurance in aircraft manufacturing.

Machines doi: 10.3390/machines14080903

Authors: Feifei Liu Wei Li Hengrui Li Jiaxin Gao Junjie Liu Wenhong Huang Qingwen Lin

To address the difficulty of obtaining real fault data from elevator traction drive systems and the potential overestimation caused by random window splitting, this study uses two public motor-drive datasets to examine how validation protocols, signal modalities, and noise conditions affect diagnostic evaluation, rather than to claim direct validation of field performance in actual elevator systems. A PMSM inverter-drive fault diagnosis dataset is used as the main dataset to evaluate multiclass classification performance based on electrical, thermal, and derived features. A multimodal MOTOR dataset is used as an independent secondary dataset to analyze the effects of validation protocols, signal modalities, and noise disturbance on model performance. The results show that Random Forest achieves a Macro-F1 of 0.9901 under random splitting on the PMSM dataset. On the MOTOR dataset, the Macro-F1 reaches 0.9682 under random splitting but decreases to 0.5856 under strict block-split validation, indicating that random window splitting may substantially overestimate generalization performance for continuous signal data. The modality ablation results show that the vibration-only modality performs best under strict block-split validation, with a Macro-F1 of 0.6420, whereas the noise analysis indicates that this modality is sensitive to disturbance. The results show that public motor-drive data can provide a reproducible methodological test bed for studying evaluation bias and signal reliability, but they should not be interpreted as direct evidence of diagnostic performance in actual elevator systems.

Machines doi: 10.3390/machines14080902

Authors: Manqiang Liu Yongjian Wang

To address weak fault signatures, unavailable target-speed sample labels, and spectral distribution shifts between source- and target-speed domains in current-only cross-speed fault diagnosis of induction motors, this paper proposes an unsupervised domain adaptation method integrating FFT-LPS, MSDRNet-1D, and DAN. First, three-phase stator-current window samples are transformed into FFT log-power spectra to highlight sidebands around the fundamental frequency, harmonic structures, and local band disturbances. Next, MSDRNet-1D, a multiscale depthwise-separable residual network for one-dimensional current spectra, extracts local spectral peaks, neighboring-band disturbances, and cross-band correlations. Finally, a DAN mechanism based on multi-kernel maximum mean discrepancy aligns deep feature distributions under labeled source-speed and unlabeled target-speed conditions. Experiments on the self-built motor-current dataset and the IEEE DataPort public three-phase induction-motor broken-rotor-bar dataset show that the proposed framework achieves the best overall performance among the compared methods and remains effective under noise and load variations. The results indicate that compact spectral representation, progressive multiscale feature extraction, and statistical domain alignment are suitable for current-based motor fault diagnosis under variable operating conditions.

Machines doi: 10.3390/machines14080901

Authors: Lihai Chen Zhenshui Li Ao Tan Yican Li Dong Jia Fang Yang Zhidan Zhong

To address the problems of severe feature coupling, difficult fault information extraction, and insufficient recognition accuracy for cylindrical roller bearings under multiple fault conditions, this paper proposes a multi-fault pattern recognition method based on a Northern Goshawk Optimization algorithm improved by refraction opposition-based learning and the sine–cosine algorithm (RSNGO). The RSNGO is used to optimize variational mode decomposition (VMD) and a convolutional neural network–bidirectional long short-term memory–self-attention (CNN–BiLSTM–SAT) network. First, RSNGO adaptively optimizes the number of decomposition modes and the penalty factor of VMD, and selects the optimal intrinsic mode function (IMF) components, from which time-domain statistical features are extracted to construct the sample set. Then, a CNN–BiLSTM–SAT diagnostic network is constructed, and RSNGO is employed to jointly optimize its key hyperparameters, including convolution kernel size, number of convolution kernels, number of BiLSTM hidden units, and initial learning rate. In this network, CNN extracts local features, BiLSTM models temporal dependencies, and the self-attention mechanism enhances the representation of critical fault features. Finally, the constructed feature samples are input into the optimized network to realize multi-fault pattern recognition of cylindrical roller bearings. Experimental results demonstrate that the proposed method effectively improves the separability and recognition accuracy of multi-fault features, exhibits strong robustness and generalization capability under complex operating conditions, and provides an effective solution for intelligent bearing fault diagnosis.

Machines doi: 10.3390/machines14080900

Authors: Shufeng Wang Yuhang Wang Yongxin Lei Lu Jin

Due to the absence of traffic signal control and the difficulty in accurately estimating the future movements of surrounding vehicles, autonomous vehicle decision-making faces challenges at unsignalized intersections. This study proposes a trajectory prediction-aided deep reinforcement learning framework. First, a composite prioritized replay mechanism is introduced into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, jointly considering temporal-difference error and reward-based event severity to enhance critical-experience reuse. Second, a convolutional multi-layer long short-term memory (CM-LSTM) model predicts surrounding-vehicle trajectories through convolutional local-motion encoding and stacked LSTM temporal modeling, and the predicted trajectories are incorporated into the deep reinforcement learning state representation. A multi-objective reward function is designed to balance collision avoidance, passing efficiency, lane keeping, and task completion. In CARLA go-straight and left-turn tests, CLS-TD3 achieves success rates of 93.8% and 90.2%, collision rates of 2.5% and 4.2%, and average passing times of 5.18 s and 5.58 s. Compared with TD3, the success rates increase by 6.3 and 8.6 percentage points, while average passing times decrease by 18.8% and 20.5%. These results demonstrate that the proposed framework improves the safety and crossing efficiency of autonomous vehicle decision-making at unsignalized intersections.

Machines doi: 10.3390/machines14080899

Authors: Jinwoo Kim Seongjin Yim

This study proposes a dual-modal preview-control framework that combines two filtered-x least-mean-square (FxLMS) algorithms with a Lotus-type modal-force transformation for active suspension systems. Using previewed road information as a common reference, the heave- and pitch-mode FxLMS controllers independently generate a generalized vertical force and pitch moment to reduce sprung-mass vertical acceleration and pitch rate, respectively. These modal commands are mapped to the front and rear actuator forces through a full-rank modal-force transformation defined from the half-car geometry. Although the baseline and proposed architectures use the same two physical actuators, the proposed controller replaces the baseline’s single adaptive rear-force correction with two independently adapted generalized commands, thereby separating the prescribed heave and pitch commands at the command-allocation level. Performance was assessed using conventional ride-comfort and motion-sickness dose measures together with supplementary visual-task-weighted indices. CarSim–MATLAB/Simulink co-simulations were conducted under four selected road-input conditions. Compared with the selected baseline, the proposed architecture produced lower vertical-motion indices in most cases, whereas the relative pitch-response benefit depended on the road input and evaluation metric. These results support further investigation under constrained and experimentally validated conditions.

Machines doi: 10.3390/machines14080898

Authors: Samir Abood Mayyadah Sahib Ibrahim Annamalai Annamalai Mohamed Chouikha

The convergence of industrial communication networks and electric drive systems has increased the range of risks associated with induction motor operation, with abnormal motor operation potentially arising from either physical motor faults or malicious cyber operations. This study examines the impact of coordinated network attacks on a three-phase induction motor drive system in a real-time laboratory environment through a PLC–SCADA controlled environment. The following operating conditions were investigated experimentally: normal operation, stator disturbance, rotor abnormalities, false data injection attack, replay-based communication manipulation, and cyber-physical events. In the attack scenarios, significant differences were observed in motor speed, electromagnetic torque, stator current distortion, and communication latency compared with normal operating conditions. To differentiate between actual machine failures and cyber-induced anomalies, an explainable AI-based diagnostic framework was introduced that employs both electrical and network-layer features. Experimental results revealed that the proposed model achieved an overall classification accuracy of 93.17%, with precision and recall > 97% across most operating classes. When subjected to a coordinated attack, communication latency rose from 4.8 ms under normal operation to 37.6 ms, and the current THD increased from 3.2% to 14.7%. The proposed framework also successfully distinguished cyber-attack-induced abnormal behavior from genuine motor faults at a 96.9% detection rate, which is lower than that of conventional AI classifiers. Explainability analysis also showed that the top features that affect the diagnostic decision process were packet delay, stator current distortion, torque oscillation, and rotor speed deviation. The results demonstrate the necessity of incorporating cybersecurity awareness into intelligent fault diagnosis systems for modern induction motors in industrial cyber-physical environments.

Machines doi: 10.3390/machines14080897

Authors: Donghui Xu Yanqi Guan Chongmin Jiang Rui Fan

To reduce ram deflection and positional deviation at the boring spindle front end of a floor-type milling and boring machine under large extension, a ram-boring spindle assembly was investigated. A finite element model was established based on the actual structure, guideway support, and spindle assembly. Gravity-induced deformation and vertical displacement at the boring spindle front end under different extension conditions were analyzed to identify weak regions. The Solid Isotropic Material with Penalization method was then used to optimize the adjustable internal region of the ram with the objective of reducing structural compliance. The topology–density distribution and the deformation characteristics under different extension conditions were then used to guide an engineering reconstruction of the internal stiffeners, considering casting, assembly, internal space constraints, and engineering experience. Static analysis, displacement testing, and modal analysis were performed to verify the optimized structure. Results show that the maximum total deformation under simultaneous maximum extension decreased from 0.10268 mm to 0.097325 mm, while the mass decreased from 2424 kg to 2363 kg. The measured vertical displacement decreased from 0.114 mm to 0.108 mm. The first six natural frequencies increased by 1.48–5.91%. The proposed reconstruction improves deformation control and dynamic performance while reducing mass.

Machines doi: 10.3390/machines14080896

Authors: Shilong Liu Yihong Tian Eduardo Galvan Juan M. Carrasco Yanchen Zhai Pengcheng Zhu Wentao Zhang Sergio Vazquez

In single DC-link current sensor-based phase current reconstruction for permanent magnet synchronous motor (PMSM) drives, the current reconstruction dead zone caused by insufficient active voltage vector duration restricted by driver dead time, switching settling, and Analog to Digital Converter (ADC) latency degrades current sensing accuracy, particularly in low modulation and sector boundary regions. Conventional phase shift methods, while extending the sampling window through Pulse Width Modulation (PWM) pattern modification, inevitably introduce asymmetric switching sequences that generate additional phase current harmonics and may reduce the linear modulation range. This article analytically characterizes the dead zone formation mechanism across the space vector plane and proposes a cross-cycle compensation strategy based on vector approximation. The method replaces the reference voltage vector with the nearest measurable vector in the present switching cycle and compensates for the resulting voltage error in the subsequent cycle, thereby extending the sampling window while preserving precise volt-second balance without extra hardware. Experimental results demonstrate that the proposed method eliminates the current reconstruction dead zone, achieves high-fidelity phase current reconstruction, and ensures robust dynamic performance under various load conditions and transients. The feasibility and effectiveness of the single current sensor drive are thoroughly validated.

Machines doi: 10.3390/machines14080895

Authors: Dong-gi Hong Tae-wan Kim

Conventional hole finishing requires separate drilling and top/bottom chamfering, and excessive insert pressure can leave scratch-type marks on the hole wall. This study develops a clip-spring-based tool that performs drilling and top/bottom chamfering in a single machining cycle, with the chamfer depth passively set by equilibrium between the hole-wall reaction and the restoring force of a replaceable clip spring. A dual-angle insert–clip-spring interface produces a non-monotonic force drop followed by a low-incremental-stiffness plateau, separating high-force burr engagement from lower-force hole passage. Four insert-geometry and spring-bottom-shape combinations were analyzed by nonlinear finite element analysis, and the two embossed-bottom cases were supported by compression tests. The dual-angle/embossed case showed a 72% Force Dip, which compression testing reproduced together with the low-force plateau, and one-step machining confirmed process feasibility. The measured hole-wall roughness decreased fourfold, from Ra 1.7 μm to 0.4 μm. These results demonstrate a passive geometric route to Force Dip generation and self-equilibrating depth setting under the tested condition.

Machines doi: 10.3390/machines14080894

Authors: Qiangwei Bao Xi Zhang Shuo Su Feiyan Guo

Human-centric manufacturing is reshaping collaborative production systems by repositioning human capabilities, safety, experience, and well-being as central concerns in the design and operation of intelligent manufacturing. As manufacturing moves toward Industry 5.0, task allocation in assembly-oriented collaborative systems is no longer only a matter of productivity, cycle time, or resource utilization, but also a key mechanism for protecting worker safety, workload balance, ergonomic compatibility, and long-term well-being. Although existing reviews have addressed various aspects of collaborative manufacturing and ergonomics, a systematic synthesis of how ergonomics is embedded into task allocation for collaborative assembly remains limited. To address this gap, this paper systematically reviews 95 studies identified from WoS and Scopus using an expanded keyword-based search strategy, with April 2026 retained as the publication eligibility cutoff. Studies were included when ergonomics or related human-factor considerations materially influenced task allocation, task assignment, planning, scheduling, or line-balancing decisions in AI-enabled and robot-assisted collaborative manufacturing, with emphasis on assembly-related settings such as workstations, workcells, and assembly lines. The literature is analyzed from four perspectives: ergonomic objectives, allocation scenarios, temporal responsiveness, and computational approaches. Given the heterogeneity of modeling, optimization, simulation, and design studies, a narrative synthesis rather than meta-analysis was conducted. The results show that physiological ergonomics remains the dominant dimension, accounting for 70 of the 95 studies. Recent studies increasingly incorporate multidimensional ergonomic risks, fatigue progression, worker trust, human preference, and real-time human-state information into allocation decisions. The reviewed studies also indicate a transition from static and assessment-informed allocation toward adaptive, state-aware, and cyber-physical allocation. Finally, the review identifies future directions concerning multidimensional ergonomic modeling, assessment-to-decision transformation, real-time adaptive allocation, human-centric interaction, and transferable industrial validation for collaborative assembly systems. No review registration was undertaken.

Machines doi: 10.3390/machines14080893

Authors: Fernando Ramírez-Paredes Juan Carlos Paz Edgar Lema

Accurate prediction of cutting forces remains a key challenge in metal machining due to the strong coupling between geometry, material behavior, friction, and thermal effects, which limits the generality of purely empirical models. In this study, physically interpretable models with predictive capability for cutting and thrust forces are developed for orthogonal cutting of AISI 1045 steel using a dimensional analysis framework based on the Buckingham Π theorem. Experimental data collected from the literature are used to construct dimensionless formulations incorporating geometrical, kinematic, mechanical, and thermal parameters. The proposed models are calibrated and evaluated using statistical performance metrics and residual analysis, and subsequently validated against independent experimental datasets not used during model development. A correction factor associated with the tool–chip contact length is optimized during validation to improve predictive accuracy. Results show that both force components can be consistently represented through a reduced set of governing dimensionless groups, providing physically meaningful scaling across a wide range of cutting conditions. The validation results confirm the robustness of the proposed formulation, while also revealing different sensitivities of cutting and thrust forces to contact, thermal, and geometrical effects. A physical interpretation of the dimensionless groups is presented, framing the machining process as a case of severe plastic deformation under high strain rates and strong thermomechanical coupling. The study demonstrates that dimensional analysis offers a physically consistent, scalable, and transferable approach for modeling cutting forces, with potential applicability to other materials and machining configurations.

Machines doi: 10.3390/machines14080892

Authors: Shengyou Zhou Wen Cui Wanli Bai Shiming Chen Weixing Hua Zhaojie Wu Yan Chen

With the growing global fleet of motor vehicles and rising demand for unmanned services, enhancing the efficiency and intelligence of refueling operations at gas stations has become a critical industry priority. This review focuses on refueling robots as its core research subject, providing a systematic review of its developmental history and system architecture. Building upon this foundation, this review conducts an in-depth analysis and synthesis of three key enabling technologies: (1) the end effector—integrating multi-degree-of-freedom actuators and sensor modules to precisely control fuel tank lid actuation and fuel nozzle insertion/removal; (2) refueling interface identification—enabling vehicle-type classification, refueling interface location extraction, and recognition of refueling interface features; and (3) refueling interface localization—determining the 6 DoF pose of the refueling interface relative to the robot. Through this technical analysis, it is shown that refueling robots have attained an initial level of intelligence; however, significant challenges remain in achieving high precision and robust performance, ensuring safety and reliability, and establishing standardization and broad interoperability. Future research efforts should therefore prioritize improving environmental adaptability—particularly in complex, unstructured settings—advancing autonomous decision-making capabilities, and enhancing product universality, thereby accelerating the commercial deployment of refueling robots.

Machines doi: 10.3390/machines14080891

Authors: Jianxin Zhang Guixiang Shen

Sliding-window few-shot evaluations can place support and query windows over shared raw samples. We audited episode-internal overlap, source-noise policy, normalization, and fixed-feature construction using Case Western Reserve University (CWRU) data and a HUSTbearing cross-speed task reconstructed from raw files. In the primary CWRU 0 hp to 3 hp (0 to approximately 2.24 kW), 10-way, 5-shot evaluation at a signal-to-noise ratio (SNR) of −5 dB across 20 seeds, common-pool construction increased log-compressed fast Fourier transform prototype (Log-FFT) accuracy by 3.83 percentage points (95% confidence interval (CI): [+3.63, +4.02]). The corresponding increase for the clean source-supervised cross-entropy prototype encoder (Source-CE-Proto) was 0.95 percentage points and was not statistically distinguishable from zero (95% CI: [−0.004, +1.90]; exact p = 0.05084). Under raw-index-separated evaluation, leave-one-SNR-out Source-CE-Proto achieved 93.23% without direct −5 dB source exposure, compared with 82.43% for Log-FFT. A No-log FFT control achieved 99.09% in the primary task and also exceeded the learned encoders in a second CWRU load pair. HUSTbearing likewise showed channel-dependent fixed-feature accuracy and overlap effects. These results do not support a general method ranking; they support reporting raw-index provenance, source-noise distributions, normalization, and exact feature construction before interpreting method rankings in few-shot bearing fault diagnosis benchmarks.

Machines doi: 10.3390/machines14080890

Authors: Zhe Shen Jisheng Han Weifeng Tang Guangsheng Wang

Driven by the rapid development of automotive fuel cells, compact and lightweight system demands require upgraded centrifugal air compressors, as conventional high speed motors at 50,000–80,000 rpm within 15 kW fail to meet current specifications. Since motor size and weight are dominated by torque, raising the rotational speed to improve power density has become the mainstream direction for ultra high speed permanent magnet synchronous motors (HSPMSMs), targeting over 100,000 rpm and 30 kW for fuel cell air compressor applications. This paper presents an 18-slot 2-pole HSPMSM with parallel magnetization, which achieves 35 kW rated power at 100,000 rpm. Electromagnetic performance, rotor mechanical strength, thermal behavior and rotor dynamics are systematically optimized by simulations and experiments.

Machines doi: 10.3390/machines14080889

Authors: Jiashuo Yu Hanbin Xiao Min Liu Dinglong Zhu

Conventional data-driven networks for gearbox fault diagnosis process multi-sensor streams as isolated sequences, failing to capture spatial-topological kinetic correlations and structural energy propagation pathways governed by multi-stage gearbox dynamics. To address these limitations, this study proposes a graph neural network-based fault diagnosis methodology integrating multi-dimensional attention and dynamic topological priors (KT-GNN-CBAM). Mesh stiffness characteristics are analytically evaluated to initialize physical topology edge weights, while a convolutional block attention module filters spatio-temporal features to suppress background noise. Node features are subsequently aggregated through a parallel dual-stream architecture comprising a physics-prior kinetic stream and a data-driven attention stream, which are dynamically fused via an adaptive gated mechanism. Experimental validation on the HP-GBS-2023 testbed under mixed operations and 6 dB noise shows that the proposed framework achieves an optimal diagnostic accuracy of 98.87%. Ablation evaluations confirm that omitting the mechanics-driven prior branch induces a 282.30% relative surge in the model’s misclassification rate. Ultimately, embedding mechanical invariants as a physical inductive bias mitigates purely data-driven black-box constraints, offering an interpretable and robust solution for advanced intelligent fault diagnosis in complex gearbox systems.

Machines doi: 10.3390/machines14080888

Authors: Timothy Reinhart Seshasai Srinivasan Zhen Gao

Synthetic data generation is a potential solution for addressing limited data in manufacturing defect detection. In injection molding of automotive tubes, surface defects such as splay present as white or silver streaks in the tube’s texture, that are difficult to capture in sufficient quantity for training robust object detection models. This study proposes Fast-DG2GAN: a DG2GAN based, computationally optimized, generative style defect generator for manufacturing defect images. Fast-DG2GAN achieves a 56% reduction in training time relative to the original DG2GAN, completing training in 334.9 min compared to 768.0 min, while maintaining comparable image quality metrics with a best FID score of 132.83 and IS 1.45 ± 0.08. Key contributions to this DG2GAN variant include depth-wise separable convolutions, reduced residual blocks, and automatic mixed precision (AMP) training, to improve training time. Training stabilization techniques include perceptual loss, feature matching, and exponential moving average of weights (EMA). Legacy Generative Adversarial Network (GAN) architectures are benchmarked for feasibility and include WGAN, DCGAN, and FastGAN, for fine-grained defect image generation essential to downstream object detection. Metrics, such as Inception Score (IS) and Fréchet Inception Distance (FID) are used for quantitative performance evaluation. This study highlights the potential of GAN-generated datasets to augment real-world training for defect detection models.

Machines doi: 10.3390/machines14080887

Authors: André Sousa António Gomes Ricardo Torcato José Mota

E-cargo bikes have emerged as a promising solution for sustainable urban mobility and last-mile logistics. However, their structural design must ensure durability and safety under demanding cargo transport and daily operating conditions. This study evaluates the structural performance of three single-track E-cargo bike frame typologies, Urban, Long John and Long Tail, using finite element analysis under fatigue and impact loading conditions derived from EN 15194:2020 and EN 17860-2:2024. Numerical models of aluminum 6061-T6 frames were developed to simulate cyclic pedaling, horizontal, seat-post and vertical cargo loading forces, together with falling-frame and falling-mass impact tests. Structural performance was assessed through fatigue life, stress distribution, damage initiation, plastic strain and permanent wheelbase deformation. The Urban and Long John frames satisfied the adopted fatigue-life requirements, whereas the Long Tail frame failed the vertical loading-area fatigue test with a predicted fatigue life of 5.22 × 104 cycles, below the required 2 × 105 cycles. The maximum von Mises stresses during the falling-frame impact test reached 384 MPa, 326 MPa and 356 MPa for the Urban, Long John and Long Tail frames, respectively, while the corresponding permanent wheelbase deformations remained limited to 2.07 mm, 1.97 mm, and 1.43 mm, all below the acceptance criterion. These results highlight the influence of frame geometry and cargo location on structural behavior and support future frame optimization.

Machines doi: 10.3390/machines14080886

Authors: Giuseppe Dipace Emiliano Mucchi Gianluca D’Elia

This paper presents a stacked neural network (StNN) framework for tool-holder health classification. The proposed architecture integrates a denoising autoencoder (DAE), used as a feature-reconstruction stage, with a multi-layer perceptron (MLP) classifier to distinguish between Healthy and Damaged tool-holder conditions. Vibration data were collected from Axial and Radial tool-holders tested under different rotational speeds and CNC machines. Six vibration descriptors—root mean square (RMS), average amplitude (AA), peak-to-peak (P2P), mean square frequency (MSF), gravity center frequency (GF), and mean spectrum amplitude (MSA)—were selected using training data only and combined with spindle speed and tool-holder type as classifier inputs. To avoid information leakage and pseudo-replication, model development and evaluation were performed using a single stratified group-wise hold-out split based on physical tool-holder units. The proposed StNN was compared with a direct MLP baseline, Random Forest, and SVM-RBF classifiers under clean/original and feature-level corrupted test conditions. On clean/original test data, the StNN achieved performance comparable to the direct MLP baseline, with balanced accuracy values of 0.8778 and 0.8694, respectively. Under feature-level corrupted test conditions, the StNN retained the highest balanced accuracy (0.8822), outperforming the direct MLP, Random Forest, and SVM-RBF baselines and showing essentially no degradation with respect to the clean/original condition. These results indicate that DAE-based feature reconstruction can preserve clean-data classification performance while improving robustness under the adopted feature-level corruption, supporting its use for feature-based tool-holder condition monitoring, with generalization assessed on unseen physical tool-holder units within a stratified group-wise hold-out split.

Machines doi: 10.3390/machines14080885

Authors: Jiandong Li Pengna Wei Jie Yang Wei Kang Shihao Zhang Qunfang Wang Wansheng Chang

The nonlinear operational stiffness characteristics of machine tool spindles directly influence machining precision and bearing service life. Traditional static stiffness tests rely on direct differential operations on raw experimental load–displacement data, which are highly susceptible to measurement noise and fundamentally fail to capture accurate nonlinear features. To address this limitation, this study proposes a physics-driven parameter identification methodology to accurately extract the static nonlinear axial stiffness characteristics of spindles under static non-rotating conditions. Specifically, the smoothness priors approach (SPA) is introduced as a robust preprocessing technique to mitigate high-frequency noise while preserving the underlying low-frequency displacement trends, thereby ensuring high-fidelity feature extraction. Subsequently, a physics-dependent spindle mechanics model is directly integrated with a two-stage hybrid optimization algorithm—combining Global Search and Pattern Search—to inversely reconstruct the actual nonlinear load–displacement relationships. Numerical simulation results demonstrate that the hybrid optimization algorithm exhibits high computational accuracy, with an identification error of only 0.67% under ideal conditions and bounded parameter identification errors within 4.68% under synthetic noise levels up to 5%. Furthermore, experimental validation conducted on a position-preloaded spindle setup under initial preloads of 507 N and 862 N yields corresponding verification errors of 12.3% and 11.6%, respectively, confirming that the proposed method can effectively extract the static nonlinear stiffness features of the spindle from noisy measurements. The results demonstrate that this approach successfully overcomes the bottlenecks of conventional techniques, providing a robust and practical tool for the precise characterization of the spindle’s static nonlinear baseline stiffness. While currently validated under static non-rotating conditions, the established framework provides a fundamental baseline for extending parameter identification to dynamic operational environments in future studies.

Machines doi: 10.3390/machines14080884

Authors: Davide Galli Chiara Nezzi Matteo Manzardo Luca Gualtieri Patrick Dallasega Renato Vidoni

Modern manufacturing is currently shifting toward highly flexible, high-mix, and low-volume production cycles, requiring small and medium-sized enterprises (SMEs) to adopt reconfigurable automation to remain competitive. However, the adoption of such technologies is often slowed down by the high cost and rigidity of commercial solutions, which typically rely on proprietary toolchains and necessitate specialized expert knowledge for reconfiguration. This study proposes a methodological framework for a modular robotic cell based on an open-architecture approach using ROS2 middleware, designed to be maintained by personnel without deep robotics expertise. The methodology emphasizes the replacement of fixed mechanical fixtures with an AI-driven perception pipeline, utilizing YOLO-based image segmentation to enable the autonomous localization of heterogeneous components. A rigorous tolerance chain analysis defines the design requirements of custom 3D-printed self-aligning fingertips, providing a mathematical and mechanical basis for ensuring assembly feasibility under tight geometric constraints. By adopting a node-based software topology, the framework facilitates rapid task reconfiguration and hardware interoperability. Experimental validation in an industrial-like environment confirms that this integrated approach provides a scalable pathway with the potential to improve cost-effectiveness in high-mix low-volume production scenarios to overcome manual production bottlenecks through intelligent, reconfigurable automation.

Machines doi: 10.3390/machines14080883

Authors: Jinyuan Wang Weilin Zhu Xiaoli Zhao Xiansong He Meile Wang Bo Yu Taowen Xiao Jianyong Yao

The non-Gaussian height distribution and power spectral density (PSD) characteristics of rough surfaces have significant effects on the real contact area, local pressure distribution, oil-film formation, and friction and wear behavior of lubricated contact interfaces in mechanical components. Conventional methods for generating non-Gaussian rough surfaces commonly rely on iterative correction under explicit statistical constraints, which limits their computational efficiency in large-scale sample generation. To address this issue, this study proposes a PSD-amplitude-constrained phase conditional variational autoencoder (phase C-VAE) for generating non-Gaussian rough surfaces. Unlike conventional constructive methods that repeatedly correct surface samples under explicit statistical constraints, the proposed method learns the conditional distribution of the Fourier phase, while the spectral amplitude used for reconstruction is directly determined from the prescribed PSD. By taking the target skewness, kurtosis, and PSD as conditional inputs, the proposed method achieves joint control of higher-order statistical characteristics and spectral characteristics within a unified generative framework. Under target conditions derived from measured surfaces, the generated non-Gaussian rough surface samples achieved mean absolute relative errors of 0.056% and 0.044% for skewness and kurtosis, respectively, with a generation time of 24.62s. These results indicate that the proposed method can effectively match the target skewness and kurtosis while maintaining good consistency between the generated surfaces and the target PSD. The proposed method alleviates the efficiency limitation of conventional constructive methods in the large-scale generation of non-Gaussian rough surface samples and provides an effective machine-learning-based generative approach for rapid batch modeling of rough surfaces in lubrication, friction, and contact analyses.

Machines doi: 10.3390/machines14080882

Authors: Muhammad Haroon Osama Kamal Rsetam Zhenwei Cao Zhihong Man

The stabilization performance of an underactuated ball and plate system (BPS) is significantly degraded by nonlinear dynamics, parameter variations, and external disturbances. This manuscript proposes a robust full-order continuous sliding mode control (FOCSMC) technique to achieve high stabilization performance and enhance the disturbance rejection capability of the BPS. First, the nonlinear dynamics of the BPS are formulated in a full-order state–space form that incorporates the dynamics of both the underactuated ball and the actuated servo motor. Second, two state differentiators (SDs) are introduced to reconstruct the unavailable state variables required for controller implementation. By avoiding direct high-order differentiation of measured signals, the proposed differentiators reduce noise sensitivity and improve practical realizability. Based on the estimated states, a continuous full-order sliding manifold is constructed to guarantee asymptotic convergence of the closed-loop system states while reducing the chattering phenomenon inherently associated with conventional sliding mode control (SMC) schemes. A Lyapunov-based stability analysis is performed to establish asymptotic convergence. Finally, comparative simulation and experimental studies under nominal conditions, parameter variations, external disturbances, and two-dimensional circular trajectory tracking demonstrate that the proposed FOCSMC consistently outperforms the hierarchical SMC (HSMC) and the continuous SMC (CSMC) schemes. In particular, the root mean square (RMS) and the maximum (MAX) of the tracking error are, respectively, 0.5653 cm and 1.9461 cm, which are experimentally achieved under the impact of the disturbances, confirming the effectiveness and practical applicability of the proposed scheme.

Machines doi: 10.3390/machines14080881

Authors: Zhe Yang Xi-Chu Wei Wen-Guang Fu Shu-Kai Li Zhen Wang

Conventional linear isolators struggle to combine high static load-bearing capacity with effective low-frequency vibration isolation. To address this limitation, this study proposes an inclined rhombic crab-inspired quasi-zero-stiffness (I-QZS) isolator. A generalized static model is established based on the segmented linkage of crab walking legs. A physics-constrained NSGA-II algorithm is used to optimize the key geometric parameters while preventing bistable snap-through by imposing a positive-stiffness constraint over the full stroke. A stiffness-compensation strategy bridges the gap between the ideal rigid-body model and the actual 3D-printed compliant structure. The dynamic response is represented by a cubic polynomial restoring-force model, and the resulting equations are solved using the incremental harmonic balance method with SVD (singular value decomposition)-based null-space continuation. Large-amplitude excitation experiments show that the I-QZS shifts the resonance peak to 0.77 Hz, reducing the peak frequency by 64.19% and the peak transmissibility by 67.06% relative to a linear isolator, while substantially broadening the isolation bandwidth. Bifurcation analysis further identifies stability boundaries for engineering design. These results provide an integrated theoretical and experimental framework for ultra-low-frequency passive vibration isolation.

Machines doi: 10.3390/machines14080880

Authors: Xinming Feng Haiming Tian Rui Zu Yi Luo Jianbo Li

Tail rotor failure represents a critical emergency in helicopter flight operations. Conventional recovery mandates an immediate engine shutdown and transition to autorotation, significantly compromising both mission survivability and operational flexibility. To achieve stable powered flight, this paper establishes a yaw stability strategy integrating vertical tail side-force control with active main rotor torque suppression. This strategy employs controlled sideslip to generate a yaw-restoring moment from the vertical tail and an increased descent rate to reduce rotor power requirement. These two effects act in concert to counteract the rotor torque. Trim analysis of a representative helicopter in a tail rotor failure condition validates the strategy. Two yaw control architectures are developed for the failure operation: (1) a cascade loop comprising yaw angle, yaw rate, lateral velocity, and roll angle, and (2) the latter omitting lateral velocity. Comparative simulations demonstrate that although Loop (1) yields slower yaw convergence than Loop (2), it delivers enhanced stability. Furthermore, an emergency control trajectory tailored for moderate forward velocity is proposed. The trajectory initiates with a controlled descent-rate increase to arrest yaw divergence. The forward velocity is then augmented to mitigate sideslip, and the descent rate is gradually reduced, ensuring adequate altitude clearance over the landing zone. This study provides a strategy for enabling powered flight under tail rotor failure conditions.

Machines doi: 10.3390/machines14080879

Authors: Zhonghao Zhang Haoying Zhou Hao Yang Gregory S. Fischer Peter Kazanzides

Robot-assisted surgery is now well established in clinical practice and has become a key technology for modern minimally invasive procedures. The da Vinci Surgical System plays an important role in this area and, through the da Vinci Research Kit (dVRK), supports advances in surgical robotics research. However, one longstanding challenge is the accurate modeling of the da Vinci system’s complex dynamics, which is relevant to research in areas such as external force estimation and control. This paper provides a structured narrative review and classification of dynamic modeling methods for the da Vinci Surgical System. We cover model-based and learning-based methods and discuss hybrid approaches as an emerging research direction. We further identify the major technical challenges and promising directions for future research.

Machines doi: 10.3390/machines14080878

Authors: Tingcheng Zong Qiang Zhang Zishan Jin Pengfei Cui Kang Yang Jinhong Song Ruiyi Zhang Junyu Wang

The solid backfilling support robot is key equipment for intelligent backfill mining, but its dual-top-beam structure, multiple closed-loop linkages and hydraulically actuated compaction mechanism make posture representation, inverse actuator mapping and control execution strongly coupled. This study develops a unified kinematic modelling and mechanical–hydraulic-control co-simulation workflow for a ZC5160/30/50 solid backfilling hydraulic support. Closed-loop vector equations are derived for the main support mechanism, rear top-beam mechanism and compaction mechanism, and the mappings among actuator strokes, posture angles and key node positions are established. An engineering posture-index system is constructed for roof contact, support adjustment and backfilling–compaction operation. Forward and inverse kinematic modules are implemented in MATLAB/Simulink and checked using an ADAMS virtual prototype. Representative workspace sampling further shows that all 15 inverse–forward verification cases converge and satisfy actuator stroke constraints, while the residual Jacobians remain full rank with maximum condition numbers of 9.135–9.950. An ADAMS-AMESim-MATLAB/Simulink co-simulation platform is then used to evaluate actuator tracking and PID-based posture-control feasibility. The numerical comparison shows good consistency for the main rigid-body posture indices, whereas conveyor-related relative-position indices show larger deviations because the suspended conveyor motion is affected by gravity in the virtual prototype. Under two target posture cases, the top-beam and compaction-mechanism angle deviations remain within ±0.3°, and the height deviation is below 5 mm. The proposed workflow provides a simulation basis for posture perception, actuator planning and control-system design of solid backfilling support robots; the reported results should be interpreted as model-level numerical consistency and co-simulation feasibility rather than physical prototype accuracy.

Machines doi: 10.3390/machines14080877

Authors: Ye Chu Henghui Liao Guo Tang Hao Chang

Fluidic oscillators (FOs) are self-excited jet-generating devices without moving parts that convert steady fluid supply into oscillatory jets through inherent flow instabilities. Among various FO configurations, dual-feedback fluidic oscillators (DFFOs) have attracted extensive attention due to their simple structure, high reliability, stable oscillation characteristics, and broad applications in active flow control, heat transfer enhancement, and fluid mixing. However, conventional trial-and-error-based optimization methods are limited by strong parameter coupling and trade-offs among multiple performance objectives, such as oscillation frequency, jet deflection angle, and energy efficiency. This review systematically summarizes recent advances in performance enhancement strategies for DFFOs from the perspective of “from geometric control to intelligent design”. The effects of multi-scale geometric regulation, including macroscopic structures, internal microstructures, and manufacturing-related factors, are discussed. Advanced optimization approaches, including active control, novel configurations, inverse design, and data-driven methods, are further reviewed. Particular attention is given to additive manufacturing challenges and DFFO performance under multiphase flow conditions, including erosion, particle deposition, atomization, and mass transfer. Finally, future perspectives are proposed regarding multi-physical coupling, intelligent optimization, and engineering applications. This review provides a comprehensive reference for the cross-scale performance enhancement and intelligent design of DFFOs.

Machines doi: 10.3390/machines14080876

Authors: Tianhao Zhu Zhenglu Shi Hui Xiao Zhihong Zeng Chao Min AL-Wesabi Ibrahim Hassan M. Hussein Farh Abdullah M. Al-Shaalan

Grid-connected photovoltaic (PV) systems require low-voltage ride-through (LVRT) to function reliably, particularly in the presence of symmetrical and asymmetric disturbances. Conventional PI-based control systems occasionally show limited resilience, particularly in the presence of distorted or imbalanced grid voltage. This paper proposes an enhanced LVRT control strategy for three-phase grid-connected PV systems by integrating a new rapid indirect Global Peak-Oriented Adaptive P&O MPPT method, referred to as (GPOA-P&O), with LADRC and DSOGI-FLL synchronization. The GPOA-P&O algorithm improves maximum power tracking by identifying the global peak and avoiding local maximum points, thereby reducing power fluctuations. Meanwhile, the cascaded LADRC controllers provide accurate voltage and current regulation, effectively suppressing DC-link overvoltage during grid disturbances. DSOGI-FLL ensures accurate positive-sequence phase-locking, enabling compliant reactive current injection even under severe voltage asymmetry, in accordance with grid-code requirements. The proposed method also eliminates second-order power oscillations and maintains constant inverter current regardless of fault severity. Case studies in 2024a MATLAB/Simulink and hardware-in-the-loop experimental platform demonstrate superior stability, fault ride-through capability, and grid-support performance compared to conventional approaches such as PI control and optimized SCSO-tuned PI.

Machines doi: 10.3390/machines14080875

Authors: Chang Sun Chenkun Wang Shiwei Huang Tianci Zhang

In industrial equipment fault diagnosis, vibration and acoustic signals are highly complementary yet exhibit significant differences in frequency distribution and noise sensitivity. Traditional multimodal methods generally rely on homogeneous feature extractors and direct feature concatenation, which may fail to capture modality-specific characteristics and introduce irrelevant information during fusion. To address this, we propose a novel multimodal heterogeneous convolutional neural network framework. Specifically, separate 1D CNN branches are designed for vibration and acoustic signals. Their architectural differences are determined by the characteristics of each sensing modality. The vibration branch focuses on extracting high-level discriminative fault features, including impulse responses and modulated components from vibration signals, while the acoustic branch is designed to preserve fragile high-frequency details of acoustic signals. Furthermore, an adaptive cross-attention fusion module is introduced to dynamically model cross-modal dependencies, assigning Softmax-based weights to enhance dominant features and suppress noise. Experiments based on bearing fault experimental data demonstrate that the proposed heterogeneous architecture significantly outperforms traditional homogeneous models. The dynamic weighting mechanism effectively prevents inferior noisy modalities from degrading overall performance, achieving high diagnostic accuracy. Although validated on rolling bearing fault diagnosis, the proposed heterogeneous multimodal framework is not restricted to bearings and can be readily extended to other intelligent condition monitoring tasks involving heterogeneous sensor fusion, such as gearboxes, motors, and other rotating machinery.

Machines doi: 10.3390/machines14080874

Authors: Borhen Louhichi Mohamed Slamani

The concept of Meta-Action Units (MAUs) has emerged as a promising paradigm for decomposing machine tool motion into fundamental action units, providing new insights into error propagation and accuracy stability in CNC machine tools. This paper presents a comprehensive review of accuracy stability from the MAU perspective. Fluctuation mechanisms induced by geometric errors, thermal effects, load-dependent deformations and wear-related degradation are systematically reviewed. Existing modeling, identification, and compensation methods are critically analyzed. A key contribution is the synthesis of a novel MAU-centric taxonomy integrating research on key MAU identification, precision remaining useful life prediction under incomplete maintenance, cascading fault propagation and reliability coupling mechanisms. The integration of screw theory, multi-body systems, and active learning Kriging is examined, along with hybrid approaches combining physics-based models with machine learning. The alignment of MAU-based digital twins with Industry 4.0 and Industry 5.0 is discussed. MAU decomposition provides a physically interpretable framework for accuracy formation. Hybrid Wiener–GPIM models achieve PRUL prediction errors below ten percent. Five research gaps are identified: uncertainty propagation, robust parameter identification, benchmark datasets, cost–benefit frameworks, and transfer learning. Addressing these gaps will guide the development of next-generation high-accuracy and intelligent CNC machine tools.

Machines doi: 10.3390/machines14080873

Authors: Meiqi Wang Yuxian Zhang Zhe Wang Yuhai Zhou Yunwen Zhang Shuofei Yang

Based on structures composed of discontinuous compression elements and continuous tensile elements, tensegrity mobile robots combine the load-bearing capacity of rigid structures with the deformability of flexible systems, demonstrating great application potential in fields such as disaster relief, pipeline inspection, planetary exploration, and operations in complex terrains. This paper systematically reviews the state of the art and development trends of tensegrity structures in mobile robotics. First, based on structural composition, existing tensegrity mobile robots are divided into four categories: rolling-gait robots, crawling-gait robots, hopping-gait robots, and multimodal locomotion robots. Their typical locomotion modes, representative advances, and application scenarios are analyzed. Second, this paper summarizes the key technological advancements of tensegrity mobile robots from aspects such as self-stress equilibrium, form-finding methods, actuation mechanisms, modelling and simulation, and control strategies, focusing on cable-actuated, rod-driven, flexible drive, position-based finite element modelling, central pattern generators, and reinforcement learning control. Furthermore, this paper points out that current research still faces many challenges, such as complex dynamic modelling and insufficient locomotion control accuracy. Finally, this paper discusses future development directions. This paper aims to provide a reference for the structural design, locomotion control, and engineering applications of tensegrity mobile robots.

Machines doi: 10.3390/machines14080872

Authors: Saber Hadj Abdallah Fatma Ben Salem Jaouhar Mouine Souhir Tounsi

This paper proposes a control method for electric vehicles’ powertrains that concentrates on improving the efficiency of the drivetrain as a whole by targeting regenerative energy recovery. The design of the system is based on a traditional six transistor voltage source inverter using an asymmetric control scheme together with a hybrid CNN-TD3 controller for speed control purposes. The asymmetric control method distinguishes the dynamics of the two operating modes of the inverter, that is, the traction and regenerative braking modes, by controlling the gain values based on the operating mode of the system. The TD3 algorithm adjusts the values of three continuous control parameters, Kpv, Kiv, and Ks, in real time. A multi-objective reward function aims to maximize the system’s energy efficiency, regenerative energy recovery efficiency, speed control accuracy, driving comfort, current harmonics reduction, and battery safety. Simulations performed using the WLTP Class 3 driving cycle show energy efficiency of 15.3% (25.2 to 21.44 kWh/100 km), 59.5% speed regulation error reduction, 19.6% increase in regenerative recovery efficiency from 65.2% to 77.34%, and 34.2% reduction in current THD from 8.58% to 5.65%.

Machines doi: 10.3390/machines14080871

Authors: Shucen Guo Jin Li Tianci Zhang

In rotating machinery monitoring, obtaining highly discriminative fault features from complex vibration signals remains a significant challenge for deep learning-based diagnostic models. In this paper, a novel intelligent fault diagnosis method named MSFormer is proposed. The MSFormer incorporates a parallel multi-scale Convolutional Neural Network (CNN) architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals. By utilizing varying kernel sizes, the multi-scale CNN extracts both high-frequency local transient impulses and low-frequency global degradation trends. Subsequently, the Transformer modules are employed to model the long-range dependencies within the extracted feature sequences, effectively mitigating the interference of environmental noise. Extensive experiments are conducted on bearing fault experimental data to evaluate the proposed method. Four state-of-the-art models are compared under the same experimental settings. Quantitative metrics and qualitative tools are utilized for comprehensive evaluation. Experimental results indicate that MSFormer achieves a 92.67% accuracy, 92.54% F1-score, and 93.22% precision, demonstrating significant superiority. MSFormer provides a powerful and precise intelligent solution for mechanical fault diagnosis.

Machines doi: 10.3390/machines14080870

Authors: Ashish Umbarkar Bhumeshwar K. Patle Sudarshan Sanap Brijesh Patel

This survey presents a large-scale, reproducible, and parameter-mapped benchmarking analysis of path planning algorithms for multiple mobile robot systems (MMRS) by systematically examining 247 rigorously filtered papers from high-impact journals. Unlike prior reviews that primarily provide conceptual taxonomies, this survey introduces execution-oriented multi-parameter mapping enabling direct comparison of classical planners (A*, D*, Cell Decomposition, APF, RM, RRT, and ORCA), nature-inspired metaheuristics (PSO, GA, ACO, GWO, FA, ABC, BFO, CS, BA, SFLA, eagle-inspired optimizers), and learning-driven AI frameworks (Fuzzy Logic, Artificial Neural Networks, and Deep Reinforcement Learning). Each paper is evaluated across 15 practical planning dimensions, including environment type (static 95% vs. dynamic 51%), multi-robot validation (52%), dynamic goal handling (13%), energy awareness (14%), timepath optimization bias (82% focus), inter-robot coordination (less than 47%), and software validation platforms (MATLAB 42% and ROS 9%), revealing that simulation-only validation dominates (98%) while experimental testing remains limited (33%). Multivariate validation through Multiple Correspondence Analysis further confirms that coordination maturity, energy awareness, and multi-robot applicability are the primary structural differentiators of deployment readiness across algorithm families. The findings emphasize the need for hybrid, energy-aware, and coordination-driven MRPP frameworks supported by experimental benchmarking and reproducible deployment pipelines to advance real-world MMRS autonomy.

Machines doi: 10.3390/machines14080869

Authors: Jinbo Liu Jintang Yang Zian Wang Kairui Chen

For multi-permanent magnet synchronous motors (multi-PMSMs) with nonlinearity and uncertainty, an event-triggered nonlinear adaptive PI control is proposed. To enhance the synchronization of multi-PMSMs, a coordinated control strategy is implemented. On the basis of this, a nonlinear adaptive PI control is proposed to address the tracking of the speed of the PMSMs, and the uncertainty signal is estimated by designing an adaptive law. Furthermore, an adaptive event-triggered control mechanism is presented to update control signals in real time, reducing communication resource consumption while maintaining ideal performance. Simulation results on a four-PMSM ring-coupled system demonstrate that the proposed event-triggered mechanism reduces the number of control signal transmissions by up to 92.1% compared with time-triggered sampling, while maintaining comparable control accuracy.

Machines doi: 10.3390/machines14080868

Authors: Julian M. Lee Edward Peter Washabaugh Vaibhav Diwadkar Sagar Buch Tyler Williamson Abhilash Pandya

Background: Robotic rehabilitation systems for upper-limb stroke rehabilitation have been developed across diverse robotic platforms, yet cross-study comparisons remain challenging due to heterogeneous system designs and classification approaches. This brief narrative review proposes a paradigm-driven framework, categorizing robotic rehabilitation systems based on underlying therapeutic interaction principles (e.g., mirror, bimanual, mirror–bimanual hybrid) rather than implementation modality alone. Methods: A structured MEDLINE/PubMed literature search was performed to identify representative studies describing robotic system characteristics, rehabilitation task structures, clinical outcomes, and mechanistic insights within mirror, bimanual, and hybrid rehabilitation paradigms. Findings: Among the reviewed studies, mirror-based systems emphasize sensory representation and interhemispheric modulation, whereas bimanual systems target coordination and motor learning through bilateral interaction. Hybrid systems integrate these approaches by combining mirrored feedback with active bilateral engagement. Emerging neuroimaging evidence, particularly resting-state fMRI, may help relate clinical improvements to neural network changes, providing a mechanism-informed perspective on rehabilitation outcomes. Conclusions: This review highlights substantial progress in robotic upper-limb stroke rehabilitation, with current systems increasingly integrating multimodal feedback and task-oriented interaction within mirror, bimanual, and hybrid rehabilitation paradigms. Limitations include variability across studies due to differences in robotic system implementation, task design, duration, stroke chronicity, and patient engagement.

Machines doi: 10.3390/machines14080867

Authors: Jindao Qiu Senxiang Lu

Shallow defects in ferromagnetic components may produce weak and unstable magnetic flux leakage responses in compact inspection devices, whereas an inductive response alone does not provide the same depth-related magnetic information. To obtain complementary defect information, this study proposes a magnetic–inductive dual-channel inspection method that integrates an MLX90393 three-axis digital magnetic sensor with a PCB planar spiral coil and an LDC1612 inductance-to-digital converter. Because the two sensing units are physically separated on the detection board, peak-position offset analysis and spatial registration were introduced to associate their responses to the same defect region. Controlled experiments were conducted on a laboratory pipeline inspection platform using a Q235 defect specimen installed at the internal inspection position of the pipe. The defects were machined on the inner surface, which was also the inspection surface. For each defect, five motor-driven axial scans were performed at 10 mm/s, with 400 samples acquired at 100 Hz during each 4 s scan. Response amplitude, signal-to-noise ratio, peak position, and repeatability were evaluated. For the square-hole defects, the magnetic response amplitude decreased from 675.0 to 98.5 a.u. as the depth ratio decreased from 50% to 10%, while the magnetic-channel SNR decreased from 30.5 to 4.7. For the 10% depth defect, the inductive channel retained an average SNR of 434.4 and a coefficient of variation of 0.23%, providing a stable auxiliary response. However, the pre-registration peak-position offset measured for this shallow defect was 21.2±20.9 sampling points, indicating substantial uncertainty in using a single magnetic extremum for scan-specific registration when the magnetic response was weak. These results demonstrate that the two channels provide different and complementary information under controlled laboratory conditions, while further validation is required for irregular corrosion, other ferromagnetic components, and practical inspection conditions.

Machines doi: 10.3390/machines14080866

Authors: Tianle Yang Yuanlin Yi Haolong Chen Zhijie Li Qin Zhou

Redundant manipulators offer greater flexibility in executing complex trajectory-tracking tasks in industrial applications, owing to their additional degrees of freedom (DOFs). However, their inverse kinematics (IK) remains computationally expensive, limiting their practical application. By combining the joint parameterization method (JPM) and the stationary point solver (SPS), the JPSP-IK framework is proposed to provide closed-form joint solutions while significantly reducing the computational burden. JPM treats the redundant variables as free parameters and analytically reconstructs the remaining joints in closed form from these variables and the target end-effector pose, thereby reducing the original full-space IK problem to a low-dimensional redundancy-resolution problem. On this basis, the SPS is developed to efficiently determine the redundant variables with respect to the secondary objective, considering joint-limit avoidance and motion smoothness, and the complete joint solution is analytically reconstructed through the analytical mapping derived by the JPM. Validation and comparative experiments demonstrate that JPSP-IK achieves substantially lower computation times than representative full-space and JPM-based IK methods.

Machines doi: 10.3390/machines14080865

Authors: Borhen Louhichi Mohamed Slamani Ilian Bonev Oleksandr Stepanenko

The positional repeatability of industrial robots is a critical yet state-dependent performance metric, highly sensitive to thermal conditioning and mechanical loading. This study develops a data-driven framework for predicting repeatability of FANUC LR Mate 200iD (FANUC, Oshino-mura, Japan) and KUKA KR 6 R700 Sixx (KUKA AG, Augsburg, Germany) robots under varying operational conditions. ISO 9283-compliant experiments using a TriCal system (TRI-CAL Ltd., Montreal, QC, Canada) were conducted across three warm-up durations, three payload levels, and five poses. Ensemble artificial neural networks with 10 independently trained networks were developed for each robot. The FANUC model achieved R2 = 0.9922, RMSE = 0.004231 mm, and MAE = 0.002979 mm, while the KUKA model achieved R2 = 0.9926, RMSE = 0.002919 mm, and MAE = 0.002215 mm. Prediction interval coverage was 93.3% for FANUC and 100% for KUKA. Per-pose R2 ranged from 0.9588 to 0.9966 for KUKA. Response surface analysis identified thermal stabilization as the dominant factor affecting repeatability, with improvements of 86% for FANUC and 84% for KUKA after 4 h of warm-up. The KUKA robot demonstrated superior robustness and lower variability compared to the FANUC robot. The framework provides a practical tool for predicting repeatability, supporting process planning, uncertainty budgeting, and precision manufacturing optimization.

Machines doi: 10.3390/machines14080864

Authors: Jong-Hyun Kim Seung-Heon Lee Soo-Bum Kim Dong-Hoon Jung Won-Ho Kim

This paper presents the integrated design and experimental verification of a ferrite spoke permanent magnet motor with rib core skew for semiconductor process pump drives. Conventional induction motors are widely used in industrial pump systems because of their robustness and cost-effectiveness; however, rotor copper loss and limited output capability under a restricted installation envelope remain practical limitations. To address these issues without rare-earth magnets, a flux-concentrating ferrite spoke rotor is applied. The proposed design procedure considers the baseline induction motor envelope, electric and magnetic loadings, manufacturable winding specifications, voltage and current density limits, irreversible demagnetization, and post-assembly magnetization feasibility. An 8-pole/12-slot topology is selected because it enables one-shot post-assembly magnetization, unlike the 10-pole/12-slot alternative requiring segmented magnetization. Rib core skew and stator tooth shoe chamfer geometries are then applied to reduce cogging torque and load torque ripple. A prototype is fabricated and tested. At 1000 rpm, the measured no-load line-to-line voltage is 21.6 Vrms. At 7000 rpm, the prototype achieves 4.028 kW output power and 93.1% efficiency. The measured post-assembly magnetization ratio is 98.7%, and the maximum winding temperature recorded during an approximately 50 min water-cooled test at 6.68 A/mm2 is 57.4 °C. These results confirm the feasibility of the proposed design procedure.

Machines doi: 10.3390/machines14080863

Authors: Bailiang Zhuang Haoqin Yang Zhongde Shan Zhuozhi Zhu Zheng Wang

Frozen sand molds exhibit broad application prospects in aerospace, large-scale complex castings, and high-end equipment manufacturing owing to their high-strength particle-bonding structure and excellent low-temperature stability. However, their brittle–plastic characteristics make them susceptible to collapse, spalling, and load fluctuations during conventional milling, resulting in nonlinear and unstable milling force behavior. To address this issue, a longitudinal–torsional resonant ultrasonic-assisted milling method was proposed, and an instantaneous milling force model incorporating the effective cutting time was established based on the elemental cutting theory and the oblique cutting force model. Through a series of milling experiments, the milling force coefficients at different spindle speeds were calibrated using the average milling force coefficient method. The identified milling force coefficient models exhibited high fitting accuracy, with coefficients of determination (R2) exceeding 0.9. The developed model was then employed to investigate the effects of various machining conditions on the milling forces of frozen sand molds. The relative error between the predicted and experimentally measured average milling forces was calculated to evaluate the prediction accuracy. The results show that the relative errors between the predicted and experimental milling forces in the X-, Y-, and Z-directions were 9.76%, 8.43%, and 8.45%, respectively, all below 10%, demonstrating the reliability and accuracy of the proposed model. Cutting depth and cutting width were identified as the dominant factors affecting the milling force, whereas the ultrasonic-assisted milling process effectively reduced the milling force, with the most pronounced load-reduction effect observed for conventionally prepared frozen sand molds. This study provides a theoretical basis and practical guidance for process optimization and parameter selection for the efficient and low-load machining of frozen sand molds.

Machines doi: 10.3390/machines14080862

Authors: Hao Huang Bing Ni Jing Huang Yiqiao Li Yali Jiang Shengqiang Shen Yali Guo

Heat pumps are core equipment for efficient low-grade thermal energy utilization and low-carbon transformation of the energy structure, offering significant energy-saving potential in building heating and industrial waste heat recovery. This paper reviews the research progress and technical challenges of compression, absorption, and adsorption heat pumps as well as nanofluid-enhanced heat transfer technology and elastocaloric heat pump systems. Air source heat pumps can delay frosting through variable frequency, heat storage, and waste heat recovery. However, accurate prediction models for performance degradation under extreme cold conditions are lacking. Although ground source and water source heat pumps exhibit significant energy efficiency advantages, ground source systems may suffer from performance degradation due to underground thermal imbalance. The application of water source systems is strictly constrained by water resource conditions. Driven by low-grade waste heat, absorption heat pumps employing traditional working pairs suffer from crystallization, corrosion, or high rectification energy consumption. The COP of a single-effect cycle under 80~100 °C waste heat is only 1.2~1.9, while hybrid cycles can reach approximately 3.2 at 120~150 °C. Although adsorption heat pumps achieve significantly improved performance under continuous heat recovery cycles, the full-scale power density of novel adsorbents such as metal–organic frameworks is inferior to the power density of traditional silica gel. Moreover, under off-design conditions, the performance drops by 23~48% compared to theoretical values. Nanofluids can enhance heat transfer, but the long-term effects of particle agglomeration at high temperatures on pump power consumption and system compatibility remain to be systematically evaluated. Elastocaloric heat pump systems can achieve refrigerant-free cooling, but current prototypes still cannot compete with traditional vapor compression systems in long-cycle fatigue reliability and power density. Current heat pump technologies generally face challenges such as insufficient adaptability to extreme conditions, bottlenecks in working fluids and materials, and a lack of long-term validation. Future research must construct a multi-source coupling optimization system, address common problems in working fluids and materials, promote long-term validation and kilowatt-level prototype demonstrations, and drive the large-scale deployment and engineering application of heat pump technology toward high efficiency, intelligence, and high reliability.

Machines doi: 10.3390/machines14080861

Authors: Jean Fulbert Ituna Yudonago Víctor Martínez Calzada Alonso Saldaña Heredia José Luis Rodríguez Muñoz Adriana Rodríguez Torres

This paper reviews numerical modeling approaches for Dielectric Barrier Discharge (DBD) plasma actuators in aircraft active flow control. While extensive experimental studies exist, a dedicated review of computational methodologies—covering macroscopic, microscopic, and empirical models—has been absent. This work systematically evaluates major models (Shyy, Suzen–Huang, Dorr–Kloker, Roth, Orlov–Corke, Massines), discussing their formulations, assumptions, computational cost, and applicability. It synthesizes simulation studies in aerodynamic applications such as separation control, drag reduction, transition delay, film cooling, and compressor stability. Key findings show that macroscopic models offer a practical balance between accuracy and cost for design-oriented studies, whereas microscopic models provide deeper physical insight at higher expense. The review highlights the effectiveness of DBD actuators in modifying boundary layers, delaying stall, and improving aerodynamic efficiency. Finally, persistent challenges are identified—including energy efficiency, scalability, and model calibration and future directions are suggested, such as hybrid modeling, multi-actuator arrays, and real-time control integration.

Machines doi: 10.3390/machines14080860

Authors: Zhenhuan Ding Jin Lv Wei Tang Kai Lv Xun Mao Qianqian Zhang

As the operation modes and maintenance management requirements of power grids become increasingly complex, the optimization of monthly outage maintenance schedules faces challenges such as difficulty in multi-department coordination, high reliance on manual experience, and insufficient adaptability to dynamic demands. To address these issues, this paper proposes a collaborative optimization method for monthly outage schedules of power grids based on a large language model and reinforcement learning. The method uses the large language model to parse natural language requirements raised in balance meetings, converting requirements such as fixed maintenance dates, duration adjustments, and forbidden maintenance periods into structured constraint parameters. Subsequently, the small reinforcement learning model based on Dueling Deep Q-Network (Dueling DQN) re-optimizes the outage schedule according to the updated environment parameters, achieving a collaborative solution from meeting requirement parsing to schedule generation. Case study results on the IEEE 39-bus system show that the proposed method can integrate the dynamic requirements arising from multiple rounds of balance meetings and perform adaptive optimization of the outage schedule under different constraint conditions. Experimental results on the IEEE 39-bus system demonstrate that the proposed framework effectively optimizes monthly outage schedules. Compared with the initial schedule, the proposed method reduces monthly voltage violations by 31 times and decreases active power loss by 35.88 MWh.

Machines doi: 10.3390/machines14080859

Authors: Yuanping He Feifei Zhao Liang Fang Ming Liao Bowen Wang Jingdong Huang Xingfu Hong

Rapid transfer of large cryogenic models between ambient and cryogenic environments causes severe thermal shocks to the dry air system, threatening dew-point stability and equipment safety. Using CFD, this study builds a 1:1 model of a cryogenic transport isolation system, including the dry hall, model carrier, temperature-conditioning room, and test section plenum. Three scenarios are analyzed: static suspension, descent to the temperature-conditioning room, and descent to the test section plenum. The effects of descent speed (1.2 vs. 2.5 m/min) and makeup air flow (0–12,500 m3/h) on temperature distribution and cable safety are examined. Results show that after 10 min of static suspension, the carrier interior averages 192 K with strong stratification and a minimum of 170 K. During descent, higher speed and larger air flow improve thermal retention. At 2.5 m/min and 10,000 m3/h, cable-adjacent gas stays above −60 °C. For the plenum, descent-matched displacement ventilation (e.g., 6000 m3/h for 1.2 m/min) keeps both the cable and the plug-in unit safe. Including the cable thermal capacity gives a smaller actual temperature drop than conservative gas-temperature estimates. This work provides numerical guidance for dry system design, operation optimization, and cryogenic protection during rapid model transfer.

Machines doi: 10.3390/machines14080858

Authors: Francisco Beltran-Carbajal Daniel Galvan-Perez Eduardo Esquivel-Cruz Hugo Yañez-Badillo Jesus C. Hernandez

Synchronous motors are widely used in applications demanding high efficiency, torque density, and precise velocity regulation, such as electrified transportation, industrial automation, and robotics. Their operation, however, is degraded by mechanical and electromagnetic disturbances that induce oscillatory load torque components, worsening velocity tracking and increasing mechanical stress. This paper presents a control-based reconstruction and harmonic characterization framework for multiple-frequency load torque disturbances in permanent magnet synchronous motor systems, addressing the order-based case in which the harmonic frequencies scale with the rotor speed. The proposed approach combines a robust velocity tracking control structure with a disturbance torque reconstruction scheme based on internal signals of the control system, so that the estimated torque acts as a virtual sensor. The reconstructed disturbance is resampled in the angular domain, where each speed order becomes stationary, and is processed by a hybrid Empirical–Variational mode decomposition with a blind order-identification stage; the Hilbert transform then yields the amplitude, frequency, and phase of each component without prior knowledge of the orders present. Unlike approaches based on additional sensors, specific harmonic observers, or detailed disturbance models, the proposed methodology relies solely on information already available within the control structure. The framework is validated on a surface-mounted and salient-pole permanent magnet synchronous motor under speed-dependent multiple-frequency disturbances, including a nonstationary Bézier load profile with amplitude-modulated harmonics, and remains reliable under measurement noise and abrupt load-torque steps. The yielded results evidence the proposed framework represents an efficient approach for condition monitoring, fault diagnosis, and predictive maintenance by accurate velocity tracking, adequate disturbance torque reconstruction, and precise harmonic characterization in synchronous-motor drive systems.

Machines doi: 10.3390/machines14080857

Authors: Min Wang Guo-Jun Qin Xiao-Fei Zhang

Vibration response analysis constitutes a pivotal approach for crack monitoring and early warning of damage identification in wind turbine blades. Traditional data-driven methods, however, demonstrate marked deficiencies in identification accuracy and generalization capability. To mitigate these issues, a method for crack damage identification in wind turbine blades is proposed, grounded in Physics-Informed Neural Networks (PINNs). Initially, utilizing a scaled-down test platform for doubly fed wind turbines, simulation experiments on blade cracks were executed. Vibration data were amassed under varying crack locations and lengths to scrutinize the intrinsic relationship between crack characteristics and the three-dimensional vibration response of the blade root bearing pedestal. Subsequently, leveraging the rotating cantilever Euler–Bernoulli beam model, the physical correlation between cracks and vibrations was dissected, and a physical information constraint model was formulated. This model was then amalgamated with a GRU-Transformer network to establish a PINN model tailored for crack damage identification. Ultimately, the model underwent testing and validation utilizing experimental data. The outcomes reveal that, in comparison to traditional data-driven models, the PINN model exhibits superior accuracy and precision in crack identification and localization, along with exceptional generalization capability and noise resilience. This research provides a novel technical pathway for enhancing the intelligence level of health monitoring for wind turbine units and holds substantial engineering significance for achieving precise condition assessment and early fault warning.

Machines doi: 10.3390/machines14080856

Authors: Shanglei Jiang Jinyang Sun Zengxiu Qin Yiqiao Li

Wave-edge milling tools can suppress chatter by introducing periodic harmonic variations along the cutting edge, which change the tooth-passing time delays between adjacent teeth. However, their stability is affected by coupled waveform parameters, such as amplitude, wavelength, and phase, making it difficult to screen suitable parameter combinations efficiently. This numerical/modeling-based study uses a previously validated multi-delay dynamic model to investigate the probabilistic relationship between pitch angle variance (PAV) and stability region area (SRA). A large number of feasible waveform-parameter combinations are generated under geometric constraints, and a PAV-based stratified sampling strategy is used to retain 54 representative parameter sets from six PAV layers for stability lobe diagram construction and SRA calculation. The results show that PAV has weak pointwise predictive capability for individual SRA values, with Pearson = 0.4234, Spearman = 0.4720, and R2 = 0.179. However, the stratified statistical results reveal a clear layer-wise probabilistic tendency: the mean SRA increases from 17.962 to 20.996 in units of rpm·m, and the probability of obtaining an above-median SRA increases from 11.1% to 88.9%. The high-value tail case with PAV > 0.06 further indicates that a higher PAV does not necessarily guarantee a larger SRA for an individual parameter set. Therefore, PAV should not be used as a deterministic predictor or stand-alone tool-selection criterion, but can serve as a low-cost auxiliary probabilistic pre-screening descriptor before high-fidelity SLD/SRA evaluation.

Machines doi: 10.3390/machines14080855

Authors: Fortuna Galdieri Mario Ortiz Antonio Esposito Eduardo Iáñez Pasquale Arpaia José M. Azorín

Sensorimotor rhythms (SMR) are widely employed in brain–machine interface applications, although their reliable detection during overt upper-limb movements remains challenging because of motion artifacts and concurrent peripheral activity. This study investigated whether SMR modulation can be decoded during elbow flexion and extension performed under active and resistive conditions resembling robot-assisted rehabilitation, with and without voluntary modulation. Nine healthy participants executed four experimental conditions while electroencephalography (EEG), electromyography (EMG), and inertial measurement unit (IMU) signals were synchronously acquired. EEG data were analyzed using a filter bank common spatial pattern (FBCSP)-based classification pipeline, whereas EMG signals were used to estimate exerted force. Subjective workload was evaluated through the NASA-TLX questionnaire. Classification accuracies generally exceeded the theoretical chance level, reaching average values above 73% ± 14% and above 80% ± 12% when discriminating voluntary SMR modulation independently of movement type. Only a few individual cases were at or below the theoretical chance level. EMG analyses confirmed higher force production during resistive tasks, while event-related desynchronization/synchronization analyses revealed distinct cortical modulations in the μ and β bands, indicating the presence of task-related cortical modulation underlying the decoded features. These findings demonstrate the feasibility of detecting SMR modulation during overt upper-limb movements and support the potential of the proposed framework for future adaptive robot-assisted rehabilitation systems, where movement resistance could be modulated according to real-time neural activity.

Machines doi: 10.3390/machines14080854

Authors: Tony Jun Tanaka Renan Sanches Geronel Rafael de Oliveira Teloli Maíra Martins da Silva

The demand for robotic manipulators has increased because of their precision, speed, and cost-efficiency in complex or hazardous tasks. Flexible robotic manipulators, unlike rigid ones, offer lower mass and energy consumption, enabling advanced applications across various fields. Despite these advantages, the mass reduction of these manipulators can lead to undesired effects, including decreased precision, increased sensitivity to parametric uncertainties, coupled dynamics, and increased oscillations caused by their inherent flexibility. Moreover, the modeling complexity of such mechanical systems represents a significant challenge, since multiple degrees of freedom must be considered. In this study, a physics-informed neural network (PINN) is designed to estimate the dynamic behavior of a flexible-link manipulator. First, a dataset is created by executing different trajectories (i.e., different rotation angles) of the flexible manipulator. Based on the dataset, the PINN is then trained using time and strain signals as inputs to estimate the angular displacement, combining a data-driven loss with a physics-based loss derived from the system’s dynamic model. Finally, the PINN model is investigated experimentally to assess the closed-loop strategy and evaluate its efficiency and reproducibility in recognizing the mechanical system behavior. Therefore, the results show that the PINN and closed-loop experimental validations are consistent with the proposed method.

Machines doi: 10.3390/machines14080853

Authors: Qingsong Lei Xianying Feng Xinting Wang Yanting Zhai Zhantao Li

Precision agriculture is a key trend in future agricultural development. Nevertheless, as a key crop in the global economy, cotton precision harvesting remains in its infancy. Therefore, this study proposes a method to precisely regulate cotton bale dimensions in packing cotton pickers, aiming to advance the precision harvesting of cotton. Firstly, based on the two basic assumptions, the relationship between the rotation angle of the rocker-arm component and the cotton bale dimension was derived. In addition, the one-to-one correspondence between the rotation angle and the dimension was demonstrated. Subsequently, a numerical case study was conducted to validate the method’s feasibility initially. However, solver failure occurred at larger angles, necessitating model refinement by incorporating the elastic deformation of the packing belt. Finally, a prototype was built and field tests were carried out. Based on the first field experiment results, the error caused by model simplification was compensated with quadratic polynomial least squares fitting. After compensating, the second field experiment results show that the absolute error in bale dimension remained below 49 mm and the relative error below 2.13%, as the diameter of the cotton bale was less than 2300 mm. This study lays a superior foundation for the precision harvesting of cotton.

Machines doi: 10.3390/machines14080852

Authors: Yifei Qiao Shiying Zhang Zinan Wang Bing Liu Jinbao Zhao Jian Zhang

During the operation of full-ceramic bearings, defects such as cracks and spalls inevitably develop on the bearing balls. These defects reduce bearing service life and compromise the stable operation of mechanical systems. To address this issue, a 12-degree-of-freedom (DOF) dynamic model of a full-ceramic bearing-rotor system (BRS) is established, considering ball crack and spalling defects. The model incorporates variations in equivalent stiffness and contact forces induced by these two defect types. Subsequently, the proposed model is solved by the Newmark–β method. Bifurcation diagrams, time-domain waveforms, and frequency spectra are employed to investigate the system’s dynamic responses. In the frequency-domain analysis, particular attention is paid to characteristic frequency components, including the rotational frequency fs, the ball spin frequency fBSF, their harmonics, and combination frequencies. Finally, an experimental test platform is constructed to validate the accuracy of the developed model. The results indicate that crack and spalling defects exert distinctly different effects on the dynamic behavior of the system. Defect width has a significant quantitative influence on the vibration response. Under various defect conditions, the prediction errors of the developed model remain within an acceptable range, with the maximum relative error of 9.05%. The developed model offers a theoretical foundation for analyzing bearing dynamics and supporting fault diagnosis applications.

Machines doi: 10.3390/machines14080850

Authors: Johannes Bolk Jan-Lukas Archut Marwène Nefzi Burkhard Corves

This paper presents a design methodology for a multi-directional quasi-zero-stiffness (QZS) system that can achieve vibration isolation of large bodies with high masses across a broad frequency range in all six degrees of freedom in space. The system is based on a QZS design combined with an elastic bedding approach utilizing helical compression springs. Unlike many existing QZS approaches, the proposed system satisfies practical application constraints as well as requirements common in ultra-precision manufacturing. Specifically, it eliminates conventional mechanical joints to prevent particle abrasion and avoids critical high natural frequencies within the isolation system itself. In a two-stage design process, the system is first designed for vibration isolation in all three translational directions and then extended to all six directions through the spatial arrangement of the springs. By analytically deriving and evaluating the system’s stiffness matrix, the mutual dependencies between the positions of the springs and the resulting natural behavior are investigated to determine a suitable system configuration. The findings demonstrate the feasibility of designing a QZS system capable of stable vibration isolation for a body with a mass of 2500 kg that can be tuned to achieve target natural frequencies of about 2 Hz in all six degrees of freedom, while providing internal natural frequencies of the isolation system above a target value of 1000 Hz. In comparison to a conventional vibration isolation system, reductions in the natural frequency of up to 80% were achieved.

Machines doi: 10.3390/machines14080851

Authors: Wei Guo Xin Li Ce Shi Jinhong Li Zemin Sun Yongjia Xu

Shaking tables are critical facilities for simulating seismic effects via ground motion reproduction. However, single-table tests are often constrained by limited platform dimensions and load capacity. While multi-table synchronization partially addresses these limitations, traditional array control methods under rigid connections face challenges, including degraded precision from synchronization errors and experimental interruptions due to output forces exceeding safety limits. To address high-precision synchronization requirements for rigid-connected dual-shaking table arrays, this study proposes an impedance-based internal force coordination control strategy. This approach enhances synchronization accuracy and helps prevent failures from excessive coupling forces. Specifically, a global simulation model and a mechanical model of the dual-shaking table array under rigid connection were established. Through simulation and experimental validation, the impact of synchronization errors was evaluated and the strategy’s efficacy was verified. Results show the strategy significantly reduces peak-force discrepancy between platforms. The method effectively circumvents experimental bottlenecks, such as output force saturation, inherently associated with rigid connections.

Machines doi: 10.3390/machines14080849

Authors: Lucia Žuľová Robert Grega Jozef Krajňák Matej Urbanský

Torsional vibrations represent a significant dynamic phenomenon in rotating mechanical systems and are often associated with increased dynamic loading, fatigue damage, noise generation, and reduced operational reliability. Conventional vibration mitigation techniques are generally effective only within a limited frequency range, which restricts their applicability in modern drivetrains operating under variable loading conditions. Consequently, increasing attention has been devoted to nonlinear vibration control concepts based on the principle of targeted energy transfer. This paper presents the development and experimental investigation of a novel TET system with variable torsional stiffness intended for torsional vibration mitigation in rotating mechanical systems. The proposed concept combines the vibration energy redistribution capability of a nonlinear absorber with adaptive stiffness tuning achieved through pneumatic elements. The torsional stiffness of the secondary subsystem can be continuously adjusted by regulating the pressure within air bellows, enabling adaptation of the system dynamics to varying operating conditions. A dedicated experimental test rig with kinematic excitation was developed to investigate the dynamic response of the coupled mechanical system and evaluate the influence of variable stiffness on the TET mechanism. The study focuses on the analysis of vibration energy redistribution, the identification of optimal operating conditions, and the assessment of the potential of variable-stiffness TET systems for wide range torsional vibration control in rotating machinery.

Machines doi: 10.3390/machines14080848

Authors: Mengjiao Min Yonggang Lu Ruiwen Ren Zequan Zhang Chengming Liu Yutong Luo Alexandre Presas

With increasing renewable energy penetration, hydropower units face more frequent load rejection transients, which impose severe hydraulic excitation on Francis turbine runners. Although extensive studies have investigated flow dynamics and stress concentrations during transients, quantitative fatigue damage assessments for runners with pre-existing cracks remain scarce. To fill this gap, this study conducts CFD simulations coupled with one-way FSI to analyze a Francis turbine runner during load rejection, comparing uncracked and cracked configurations. Fatigue damage is evaluated using rain-flow counting, a modified S-N curve with Goodman mean stress correction, and the Palmgren–Miner linear damage rule. Results show that stress concentrations shift from the band-side to the crown-side T-junction during load rejection, with 4.5 times higher fatigue damage at the crown (D = 1.62 × 10−4) than at the band (D = 3.57 × 10−5). Pre-existing cracks increase local stress and reduce the allowable number of load rejection events from 6173 to 514 cycles. Reducing residual stress from 200 MPa to 100 MPa lowers fatigue damage by approximately 42%. This study provides a quantitative framework for transient fatigue assessments.

Machines doi: 10.3390/machines14080847

Authors: Yun Wang Maoyuan Li Xun Li

Low-altitude general aviation aircraft and unmanned aerial vehicles (UAVs) are widely deployed for complex operational tasks, yet low-altitude gusts and crosswind disturbances induce severe airspeed fluctuations, leading to variable lift, unstable flight altitude, and perturbed pitch attitude. Such aerodynamic fluctuations degrade flight smoothness and increase pilot control workload. To mitigate lift and altitude instability under unsteady incoming flow, this paper proposes a novel passive trailing-edge winglet configuration capable of self-regulating wing angle of attack (AOA) without active flight control systems. A quasi-static aerodynamic equilibrium analytical model based on moment balance about the wing pivot axis is established, combined with validated Computational Fluid Dynamics (CFD) simulations to characterize the passive AOA adjustment mechanism and quantify lift variations under velocity perturbations. Results demonstrate that the integrated wing-winglet layout generates passive aerodynamic feedback moments to automatically adjust the wing AOA when freestream speed varies. For airspeed disturbances within ±10% of the cruise velocity (102 m/s, 0.3 Ma), the total lift fluctuation of the wing-winglet assembly is suppressed within ±1.01%, whereas conventional fixed-wing configurations experience lift deviations between −16% and +22% under identical disturbance conditions. Notably, the present study only verifies quasi-static aerodynamic equilibrium under steady inflow; dynamic flight stability, unsteady aerodynamic effects, and stall-limit performance remain unexamined and require further investigation. The core novelty of this design lies in the passive negative-feedback aerodynamic moment generated by the trailing-edge winglet, which decouples fuselage attitude from wing pitching motion and stabilizes equilibrium lift under mild low-altitude gust perturbations.

Machines doi: 10.3390/machines14080846

Authors: Yang Peng Shaomu Wen Yongbo Wang Kedu Ma Qin Bie Wei He

Oil and gas pipelines are the core infrastructure of energy transportation, and their safe operation is crucial to national energy security. Aiming at the difficulty of feature extraction and insufficient diagnosis accuracy of pipeline acoustic fault, a fault diagnosis method based on dual-branch parallel feature fusion of the original time-series signal and time–frequency map was proposed. In this method, the time–frequency map of the one-dimensional acoustic signal was generated by continuous wavelet Transform (CWT), and the original signal was input into the dual-branch network, respectively. The spatial–frequency domain features were extracted by using lightweight depthwise separable convolution (LDconv) embedded with coordinate attention (CA) in the upper branch. The lower branch mines local details and temporal dependencies through deformable convolution v4 (DCNv4) and Gated Recurrent Unit (GRU). The dual-branch features were concatenated and fused by Global Average Pooling (GAP), and finally the classification results were output by the fully connected network and Softmax. Experiments on industrial field data show that the average diagnostic accuracy of the proposed method is 98.87%, which can effectively extract weak fault features under complex noise, and has significant advantages in early fault recognition and generalization performance.

Machines doi: 10.3390/machines14080845

Authors: Huajun Dong Minghan Yang Xingyu Guo Zhaoyu Ku

Reliable and fast detection of steel surface defects is essential for quality control in intelligent manufacturing. In practical inspection scenarios, lightweight detectors still face challenges associated with low-contrast defect textures, the loss of fine edge information during downsampling, and insufficient interaction among features at different scales. To improve detection accuracy and computational efficiency, this study proposes TAD-YOLO11n, a lightweight multiscale feature-enhancement detector based on YOLO11n. In the backbone, C3k2-TFE-EMA is introduced to enhance the representation of weak textures and elongated defects by combining local texture enhancement, frequency-domain modeling, and EMA attention. ED-ADown is employed during downsampling to preserve edge details while reducing the parameter count and computational cost. In the neck, DynamicScalSeq+ASF is incorporated to promote adaptive interaction among P3, P4, and P5 features. Experiments on the NEU-DET dataset showed that TAD-YOLO11n achieved an mAP50 of 79.0%, exceeding the YOLO11n baseline by 4.8 percentage points. Meanwhile, the number of parameters decreased from 2.583 M to 2.106 M, and the computational cost decreased from 6.3 to 5.3 GFLOPs, while the inference speed reached 175.5 FPS. These results demonstrate that the proposed model improves detection performance under lightweight constraints and provides a practical solution for real-time steel surface defect inspection.

Machines doi: 10.3390/machines14080844

Authors: Ganggang Yin Ze Wu

Accurate tool wear monitoring can effectively improve machining quality and reduce tool costs. In this paper, tool wear monitoring was studied using multi-signal fusion based on an integrated machine learning model. Firstly, tool holder strain, acceleration, and AE signals are selected as tool wear monitoring signals based on different types of physical quantities and acceptable installation convenience. Tool wear experiments are conducted to synchronously acquire these signals. After the signal denoising process, 102 features from these signals are extracted, which include time domain, frequency domain, and wavelet packet time-frequency domain features. Then, 15 key features are selected using the minimum redundancy maximum relevance (mRMR) method to realize multi-signal fusion at the feature level. Subsequently, an integrated machine learning model is proposed for tool wear monitoring. Three complementary models, extra trees, random forest, and ridge regression, are selected to construct the integrated model. The results indicate that this strategy achieves a tool wear state classification accuracy of 96.77%, exhibiting higher accuracy than single models.

Machines doi: 10.3390/machines14080843

Authors: Miguel E. Iglesias Martínez Jose A. Antonino-Daviu Larisa Dunai María J. Picazo-Ródenas J. Alberto Conejero Humberto Michinel Pedro Fernández de Córdoba

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation.

Machines doi: 10.3390/machines14080842

Authors: Eunjin Kim Sangwon Lee Byeongjun Kim Geuntae Heo Taeyong Kuc

This study addresses an off-site construction (OSC) task: installing heavy prefabricated equipment modules at elevated positions inside existing structures. The task simultaneously demands multi-ton payload capacity, a lift height approaching 10 m, and holonomic maneuvering in narrow aisles; to the authors’ knowledge, no single reported platform satisfies all three. We present a heavy-duty, high-lift mobile robot that lifts 6 t to 8 m. Two active swerve-drive modules and three passive casters form a five-point asymmetric layout combining holonomic mobility with load distribution, and the lift unit functionally decouples the vertical stroke (four helical band actuators) from the lateral stiffness (four scissor-stabilizing mechanisms). Planar motion is partitioned into three driving modes with closed-form forward and inverse kinematics, and zero-velocity transitions remove the kinematic model mismatch and the instantaneous-center-of-rotation discontinuity of a single unified model. Prototype measurements confirmed the motor-sizing torque assumptions, and chassis finite element analysis showed a factor of safety above 2.0 under maximum payload and quantified the in-plane stress induced by kinematic mismatch. In two field deployments, the robot reduced personnel by 25.0–27.3%, equipment by 42.9–60.0%, and installation duration by 50.0–85.7% relative to the incumbent methods, thereby extending mobile robots from horizontal transport to vertical OSC module installation.

Machines doi: 10.3390/machines14080841

Authors: Bowen Si Dayong Ning Jiaoyi Hou Yongjun Gong Ming Yi Fengrui Zhang Zhilei Liu

Robotic 3D manipulation of small targets, such as laser ablation of urban-tree fruit balls, requires locally reliable depth. Mainstream RGB-D depth-refinement methods usually optimize image-wide metrics, which can leave task-relevant regions under-resolved and limit their direct use in precision robotic operation. This paper presents residual-gated RGB-D depth refinement (RG-RGD), a task-triggered local depth-refinement framework that reallocates computation toward task-relevant 3D geometry after a candidate target has been selected. The method contains three coupled designs: a self-play benefit-driven foveation mechanism that focuses network capacity on regions where refinement reduces geometric residuals; residual prediction with Bayesian measurement fusion that anchors predictions to available raw observations; and inertial measurement unit (IMU)-conditioned self-supervised training that improves inter-frame view consistency. Evaluated on the Visual Odometry with Inertial and Depth (VOID) benchmark, RG-RGD obtains competitive standard depth-completion metrics, including a mean absolute error (MAE) of 24.95 mm and an inverse mean absolute error (iMAE) of 10.85. On self-collected London plane fruit-ball sequences, the method reduces region-of-interest geometric error more strongly than full-image error. A qualitative demonstrative laser-ablation use case illustrates how refined local geometry can drive physical branch filtering, cutting-point selection, and gimbal-based execution. The results support task-triggered local depth refinement as a practical perception component for robotic manipulation.

Machines doi: 10.3390/machines14080840

Authors: Liya Ma Xingyu Chen Wei Xiong Zenghui Ma Bin Zhao

A hyper compressor is one of the most critical pieces of equipment for synthesizing low-density polyethylene (LDPE) with a discharge pressure up to 350 MPa. Such a high discharge pressure creates significant challenges for safety and reliability. In this study, a 3D transient computational fluid dynamics (CFD) model of a hyper-compressor’s interstage pipeline was adopted to examine the characteristics of pressure pulsation inside the interstage pipeline. First, flow channels of multiple poppets in the combined valve were simplified into a single channel while keeping the flow area identical. A single-degree-of-freedom equation was employed to calculate the dynamic motion of the simplified valve. Then, the acceleration, velocity, and lift of the valve were acquired by accumulating pressure-induced forces. Finally, the entire model was solved, and the p–θ diagram inside the working chambers and the pressure pulsation inside the interstage pipeline were acquired. It was found that the isotropic indexes of compression and expansion processes were 29.1 and 3.17 for the first stage of the hyper-compressor, and 36.85 and 3.65 for the second stage, respectively. The indices were significantly higher than those of the common compressor due to the higher compressibility of ethylene at hyper-pressures. The maximal pressure pulsations around the first stage and the second stage were 17.5% and 16.22%, respectively. Strain gauges were adopted to measure the on-site pressure pulsation. The results showed that the proposed CFD model was able to predict the coupling of thermodynamic processes in the working chamber and pressure pulsation in the pipeline. The comparison between the predicted and measured frequency vs. amplitude diagrams showed that the discrepancies at lower harmonics were smaller than those at higher harmonics. The maximum discrepancy of the dominant harmonic amplitudes between the CFD prediction and the strain gauge measurement was within approximately ±13%.

Machines doi: 10.3390/machines14080839

Authors: Buyun Zhang Bo Xu Sunfeng Qian Yunshun Zhang Chin-An Tan

Vehicle energy-regenerative suspension can convert part of the vibration energy induced by road excitation into electrical energy. However, there are coupled performance conflicts among energy recovery, ride comfort, and suspension safety, and a fixed-weight LQR controller finds it difficult to maintain a reasonable performance compromise under different road conditions. To address this problem, this paper proposes an AWG-NN-LQR control method based on an Adaptive Weight Generation neural network. First, a quarter-car energy-regenerative suspension model, an electromagnetic actuator model, and a random road model are established, and the vertical vehicle responses and energy-regeneration characteristics under different road classes are analyzed. Second, vehicle speed, road roughness coefficient, and statistical features of vehicle responses are used as inputs. LQR weight labels are generated through offline closed-loop simulation and candidate-weight search, and the AWG-NN is trained to learn the nonlinear mapping relationship between road conditions and weight parameters. Finally, closed-loop comparative validation is conducted for the passive suspension, fixed-weight LQR, and AWG-NN-LQR under a typical class-C road condition. The results show that, compared with the fixed-weight LQR, AWG-NN-LQR reduces the RMS of body acceleration from 1.7041 m/s2 to 1.6527 m/s2, and reduces the RMS of suspension deflection from 0.00863 m to 0.00844 m, while achieving an average regenerated power of 9.41 W. The proposed method can improve the objective-bias problem of the fixed-weight LQR under a typical operating condition while maintaining a certain energy-regeneration capability, providing a feasible approach for multi-objective adaptive control of energy-regenerative suspension.

Machines doi: 10.3390/machines14080838

Authors: Tyler Rome William Singhose Khalid Sorensen Franziska Schlagenhauf

While input shaping is an effective control technique for reducing residual oscillation in systems with continuous actuation, many systems can only be actuated at discrete time steps. Thus, to apply this control technique to discrete time systems the input shapers must be discretized. This process can reduce the effectiveness of the shapers. Furthermore, nonlinearities in frequency behavior, such as those observed in linear resonant actuators (LRAs), can further diminish the effectiveness of input shapers. This paper examines the effectiveness of previously proposed discretization approaches and seeks to develop an improved approach for creating input shapers for linear resonant actuators (LRAs). LRAs are often utilized in consumer electronics to generate haptic signals. This application presents the dual challenge of minimizing residual peak accelerations while maintaining large transient peak accelerations. Both challenges are addressed herein.