Machines doi: 10.3390/machines14050545

Authors: Dae Hyun Kim Jin Taek Chung

Blade tip leakage flow in gas turbines is associated with aerodynamic loss and local heat transfer variation in the tip region. In this study, the flow structure, total pressure loss coefficient, heat transfer coefficient (HTC), and film cooling effectiveness (FCE) were numerically investigated for a plane tip (PLN) and five squealer tip geometries: a conventional squealer tip (SQR), cutback squealer tip (CBS), multi-cavity squealer tip (MCS), triangular-grooved suction-side squealer tip (GSS), and multi-cavity triangular-grooved suction-side squealer tip (MGS). All configurations were compared under the same cascade geometry, tip-clearance condition, and inlet/outlet boundary conditions to examine the geometry-dependent relationship among aerodynamic loss, heat transfer, and film cooling performance. Film cooling was evaluated at blowing ratios of M = 1 and 2 using a camber-line hole arrangement, and the effect of hole rearrangement was further examined at the same blowing ratio and with the same number of cooling holes. The results indicate that the aerodynamic and thermal characteristics of the tip region vary with the leakage-flow path, cavity recirculation, and reattachment behavior formed by each tip geometry. Under the present conditions, SQR showed the lowest downstream total pressure loss coefficient, with a 7.27% reduction relative to PLN, whereas MGS showed the lowest geometry-normalized heat transfer rate among the tested geometries. Increasing the blowing ratio tended to increase FCE, although local cooling performance was affected by high-pressure or reattachment-dominated regions where coolant ejection, surface attachment, or lateral spreading was limited. Compared with the camber-line arrangement, the rearranged hole configuration increased local FCE by up to 29.6% for CBS and 23.3% for MGS at the same blowing ratio. These results may be used as comparative data for evaluating squealer tip geometries and cooling-hole placement during preliminary blade tip cooling design.

Machines doi: 10.3390/machines14050546

Authors: Ngoc Yen Phuong Vo Thanh Danh Le Minh Ky Nguyen

As is well known, vibration, especially ultra-low-frequency vibration, is harmful to machinery’s accuracy and service life and even human health. This paper experimentally validates vibration isolation technology for low-frequency applications based on quasi-zero stiffness (QZS) properties. Firstly, a platform for isolating low-frequency vibration, referred to as LFVIP, is introduced, featuring a quasi-zero stiffness characteristic. Then, the dynamic stiffness of this platform is analyzed and established. Based on this analytical model, a solution for designing the platform to obtain the desired stiffness in the equilibrium state is suggested. Secondly, an experimental setup is established to verify the isolation performance of the platform under base displacement excitation. In addition, the isolation effectiveness of the LFVIP is compared with that of its linear counterpart (LC). The experimental results indicate that the LFVIP provides the starting isolation for effective isolation at approximately 2 Hz, while that of LC is around 6 Hz. Moreover, the vibration attenuation of the LFVIP is greater than that of the LC. Vibration isolation technology based on quasi-zero stiffness is superior to the LC, particularly in the low-frequency region. This work offers useful insights for the design of vibration isolators, suspension systems, and related applications, particularly by demonstrating how the superior vibration attenuation of the LFVIP can be leveraged to improve the performance of these systems.

Machines doi: 10.3390/machines14050544

Authors: Viktor Masalskyi Ujjawal Malani Sigitas Petkevičius Jūratė-Jolanta Petronienė Andrius Dzedzickis Giedrius Garbinčius Vytautas Bučinskas

Modern scanning microscopes and robotic scanning systems increasingly use visual recognition and machine learning technologies to extract complex data from acquired images. This study examined sensor data fusion in optical imaging to detect and control the deviation of the position of the tool during various micro-manipulations for biologic and microscale engineering. The sensor data fusion study was performed using a scanning micro-robotic system with an integrated optical microscope and a vision sensor providing an image of the object’s bottom. The bottom vision sensor is a typical complementary metal–oxide–semiconductor sensor that is used to observe micrometer-sized semi-transparent objects. The challenge for sensor fusion in such a study is not only data fusion, but also the trajectory deviation inherent in directing the manipulator in the X and Y directions according to the selected trajectory. The data fusion method was applied to estimate deviations from the given trajectory of the scanning microscope. The unique novelty of this work is that an additional vision sensor is used to increase the accuracy of positioning determination of a scanning micro-robotic system, placed under the semi-transparent object, using the fusion of the obtained data, thus additionally controlling the objective deviations. By testing several known data fusion methods, a unique solution was achieved. The proposed sensor fusion method achieved a positioning accuracy of less than 0.5 μm at speeds up to 5 mm/s. Experimental results demonstrate that the system maintains high stability. This quantitative performance proves the system’s suitability for high-precision biological micro-manipulation, where mechanical drift was previously a limiting factor.

Machines doi: 10.3390/machines14050543

Authors: Gongxian Lou Maolong Lv

This paper proposes a novel prescribed-time formation tracking control farmework of multi-fixed-wing UAVs under external disturbance and actuator failures. As the complexity of aerial missions intensifies, achieving precise position and attitude tracking within a user-defined upper bound of settling time becomes a paramount challenge for intelligent swarm systems. Unlike traditional finite or fixed-time methods, where convergence depends on initial states or suffers from conservative estimation, the proposed approach ensures stability within a prescribed time independent of initial conditions. A key innovation is the introduction of a piecewise reference convergence differential function. This mechanism eliminates the need for state transitions, thereby reducing computational complexity while ensuring smooth tracking without control surface chattering across the entire mission. Additionally, a prescribed-time sliding mode disturbance observer is developed to provide precise and timely compensation for external disturbances and actuator faults. Rigorous Lyapunov analysis proves that all closed-loop signals are bounded and the tracking errors converge to a small neighborhood of zero within the predefined time. Numerical simulations demonstrate that, under time-varying disturbances and actuator faults, the disturbance estimation errors converge within 4 s, while both attitude and velocity tracking errors converge within 6 s, achieving fast transient response and high tracking accuracy. The UAV swarm successfully maintains the desired formation during aggressive maneuvers, including speed variations, climbing, and diving. These results verify that the proposed method provides a computationally efficient, robust, and high-precision solution for time-critical formation control of fixed-wing UAV swarms under complex uncertainties.

Machines doi: 10.3390/machines14050542

Authors: Elizabeth Delgadillo-Perez Hugo Yañez-Badillo Carlos Sotelo David Sotelo Francisco Beltran-Carbajal Jesus C. Hernandez

This paper introduces a new speed control framework design for direct current electric motors coupled with a gearhead. The proposed framework suitably integrates differential flatness, model predictive control, and Kalman filter theory for efficient speed tracking in disturbed scenarios. The performance of the proposed control framework is compared against a flat feed-forward proportional–integral–derivative controller, an extended state offset-free model predictive control, and a linear quadratic integral controller. The results show improved tracking performance with reductions of up to 60% in NRMSE and 50% in ITAE compared to the baseline controllers while maintaining a comparable and bounded control effort due to the constraints enforced in the framework formulation. These results show that the proposed approach effectively captures the dominant disturbance behaviour under varying operating conditions. Thus, it is demonstrated that the flat system formulation allows for proper speed control, providing practical insights for advanced motor control in industrial automation, robotics, and electric mobility applications.

Machines doi: 10.3390/machines14050541

Authors: Wang Zhao Wang Zhang Wang Li Kong

Closed-circuit axial piston pumps in the travel hydraulic systems of heavy-duty engineering vehicles are highly vulnerable to severe transient cavitation during emergency braking. Rapid pressure reversal at the interface between the cylinder bore and the valve plate causes volumetric efficiency loss, intensified pressure pulsation, and erosion damage; however, the coupled mechanism by which throttling, vortex formation, and cavitation interact in this region, together with its structural regulation pathway, remains insufficiently understood. To address this gap, a closed-circuit axial piston pump for cotton pickers was investigated under emergency braking as a representative extreme loading scenario. A full-passage transient CFD model was established and validated against steady-state volumetric efficiency tests on a heavy-load test bench, as well as against PIV internal flow visualization on a Reynolds-scaled transparent model. Parametric transient CFD sweeps were then performed, and a multi-objective optimization model was developed and solved using a Kriging-assisted NSGA-II algorithm with entropy-weighted TOPSIS decision-making. The results identify the interface between the cylinder bore and the valve plate as the primary cavitation zone, with cavitation driven by local throttling and wall-attached vortices rather than by global low pressure. The optimized cylinder bore configuration reduces the peak gas volume fraction by 34.7% in the total flow domain and by 15.7% in the valve plate region, while maintaining volumetric efficiency above 97.8%; the port plate pressure pulsation increases by 12.97%. The key takeaway is that targeted optimization of the cylinder bore alone, without altering the overall valve plate or piston block architecture, can effectively suppress transient cavitation, while revealing an inherent trade-off with pressure pulsation control. In conclusion, this work clarifies the cavitation mechanism, provides a validated numerical and experimental framework, and offers an implementable design pathway for transient cavitation control of closed-circuit piston pumps under extreme loading conditions.

Machines doi: 10.3390/machines14050540

Authors: Yanbo Wang Tao Shen Yongming Xu Ziyi Xu

Plunger pumps are widely used in high-pressure and high-flow applications and exhibit strong adaptability to different fluid media. In addition to the interaction between the valve and the fluid, a potential coupling effect may exist between the flow characteristics of the pump and the electromagnetic characteristics of the motor. To investigate the electromagnetic–mechanical–hydraulic coupling effect in a motor–pump system, a transient coupled dynamics model integrating electromagnetic fields (EMF), multi-body dynamics (MBD), and computational fluid dynamics (CFD) is developed. The motion of the valve is incorporated into the model through dynamic mesh and user-defined function (UDF) techniques. The different physical models are coupled through torque, speed, force, and displacement. Based on the proposed model, the coupling characteristics of the system are analyzed. The results show that pulsating components associated with the reciprocating frequency appear in both the rotational speed and torque of the motor, resulting in fluctuations of approximately 2.11% in speed and 29.57% in torque. These pulsations are also reflected in the stator current spectrum. In addition, the valve motion at different crank angles and the flow patterns in the pump chamber are analyzed. The electromagnetic characteristics of the motor have a limited influence on the internal flow behavior of the pump.

Machines doi: 10.3390/machines14050539

Authors: Yudong Zhang Biao Ma Kun Liu Liang Yu Jing Zhang Run Mao Hanqiao Sun

Planetary bearings are critical components in double planetary gear trains. The influence of carrier rotation direction on bearing dynamic behavior remains insufficiently understood, which hinders accurate reliability assessment and optimal design. To investigate this issue, a dynamic model of the double planetary gear train is developed. The model captures the coupled interactions and motion characteristics of both gears and bearings. Furthermore, an experimental platform is constructed to validate the accuracy of the proposed model. A comparative analysis is conducted to examine the dynamic loads and vibration responses of the planetary bearings under forward and reverse carrier rotations. The results show that reverse rotation significantly intensifies collision forces, particularly under low-speed and high-torque conditions, where the increases for inner and outer bearings reach 38.34% and 31.25%, respectively. In terms of contact forces, the inner bearing exhibits higher loads under reverse rotation, whereas the outer bearing carries greater loads under forward rotation. Vibration response analysis reveals that the carrier rotation direction has a limited effect on the vibration of the inner bearing, but significantly amplifies that of the outer bearing. Under reverse rotation, the acceleration amplitudes of the outer cage in the x- and y-directions increase by 96.20% and 95.74%, respectively, markedly exceeding the approximate 26% increase observed for the inner bearing. This study provides new insights into the asymmetric tribological behavior of planetary bearings under bidirectional rotation. These findings provide theoretical guidance for the design and optimization of planetary bearings in double planetary gear trains.

Machines doi: 10.3390/machines14050538

Authors: Guanglin Li Jing Zhao Xiaoqing Guan Zhizhou Chen Bin Wang

Interior permanent magnet synchronous motors (IPMSMs) offer performance advantages such as saliency effect, high mechanical strength, and a wide speed regulation range. The magnetic and mechanical properties of the core material significantly influence IPMSM performance. By investigating the effects of different core materials on IPMSM performance, an optimal material combination can be identified to enhance the overall motor performance. This paper takes a V¯-shaped IPMSM for use as a main drive motor in new energy vehicles as the research object. First, the influence of the iron loss characteristics of non-oriented silicon steel (NOSS) on IPMSM performance is analyzed, and the material selection principles for the stator and rotor cores under this condition are summarized. Subsequently, the influence of the magnetic flux density characteristics of NOSS on IPMSM performance is analyzed, and the corresponding material selection principles for the stator and rotor cores are summarized. Furthermore, ultra-high-yield-strength NOSS is applied as the motor core material to reduce the width of the rotor magnetic flux barrier, and the resulting performance advantages for the IPMSM are analyzed. Finally, prototypes of the IPMSM are manufactured and tested to validate the results of the analysis.

Machines doi: 10.3390/machines14050537

Authors: Ming Li Jia Yao Haoyuan Chen Hongwei Hu Yalun Liu Yanlong Liu Wenhang Xu Hongtao Lu Zhao Guo

To address the high physical demands faced by personnel engaged in power maintenance operations, this study develops a hip assistive exoskeleton capable of state recognition between level-ground walking and transmission tower climbing. The mechanical structure of the exoskeleton is designed based on motion data analysis of human level-ground walking and tower climbing activities. A dynamic model of the human lower limb is conducted to support state-based torque control of the actuators. To accommodate different locomotion scenarios, a control strategy based on a hierarchical finite state machine (HFSM) is proposed to achieve adaptive state recognition and enable the exoskeleton to provide state-specific torque output. State recognition and transition experiments, alongside laboratory and field transmission tower climbing experiments, are conducted. The results show that the exoskeleton can reliably recognize transitions between walking and climbing, providing effective assistance during transmission tower climbing operations. Furthermore, laboratory and field transmission tower climbing experiments show that exoskeleton assistance reduces integrated EMG (IEMG), root mean square (RMS) and maximum absolute value (MAXABS) values of the biceps femoris (BF), rectus femoris (RF), and vastus medialis (VM), demonstrating the effectiveness of the exoskeleton.

Machines doi: 10.3390/machines14050536

Authors: Tianpeng Zheng Zhongming Xiong Zhihao Yuan Zhenguo Li

Under ideal trapezoidal back electromotive force (EMF) conditions, a brushless direct current (BLDC) motor can produce constant instantaneous electromagnetic torque when supplied with ideal three-phase square-wave currents. However, this operating mode may result in relatively high copper loss. In practical applications, where both the back-EMF and the current waveforms deviate from their ideal shapes, significant torque ripple is introduced. To address these issues, this paper proposes a maximum torque per ampere (MTPA) control strategy for BLDC motors based on zero-sequence current injection. An improved Park (3s–3r) is employed to develop the mathematical model, in which the synthesized non-zero-sequence components are mapped exclusively onto the q-axis. By properly regulating the d-axis and 0-axis reference currents, the proposed strategy achieves minimum copper loss operation. Based on this framework, a torque control system incorporating zero-sequence current injection is established to further enhance performance. The feasibility and effectiveness of the proposed control strategy are validated through digital signal processing (DSP)-based experimental results.

Machines doi: 10.3390/machines14050535

Authors: João Matos Coutinho Hugo Raposo José M. Torres Farinha Antonio J. Marques Cardoso

The paradigm transition of the life cycle management of physical assets in the railway sector demands new maintenance models that imply the conventional predictive approaches to be surpassed. This paper proposes an innovative methodology that integrates Building Information Modelling (BIM) with predictive maintenance (PdM) systems to be applied to rolling stock and, in this way, be enhanced by Generative Artificial Intelligence (GenAI). The research focuses on the autonomous synchronisation of the Rolling Stock Digital Twin (DT). Unlike static BIM models, the proposed solution enables the use of GenAI algorithms to process continuous data streams from integrated sensors, allowing the digital model to evolve autonomously as physical wear occurs. In this framework, GenAI (via Generative Adversarial Networks—GANs) is essential for data augmentation, enabling the simulation of rare “long-tail” failure events that are scarce in real-world historical data. By synthesising these degradation scenarios, the model learns complex mechanical collapse patterns that otherwise would be ignored by traditional PdM approaches. GenAI is employed to synthesise degradation scenarios, perform real-time parametric updates within the IFC (Industry Foundation Classes) schema, and optimise maintenance workflows. The application of this framework demonstrates a significant reduction in diagnostic latency and optimises the rolling stock’s operational life cycle by automating updates and reducing the need for manual data entry. This study concludes that the convergence among BIM, PdM, and GenAI establishes a robust framework for railway fleet management. While the current validation focuses on bogie systems using Random Forest and LLMs, it paves the way for a future Industrial Metaverse where immersive diagnostics can be integrated into the maintenance lifecycle.

Machines doi: 10.3390/machines14050534

Authors: Braden McLain Remy Mathenia Todd Sparks Frank Liou

This work presents a machine-vision–based measurement and control framework for laser wire directed energy deposition (LWDED) processes. A visible-light camera system is used to capture meltpool images, from which a novel vision algorithm extracts the wire–meltpool interface location. By utilizing a camera that is rigidly mounted to the deposition head, the vision algorithm provides a relative measurement of the distance between the nozzle tip and the workpiece, also referred to as wire stickout. A proportional-derivative (PD) control strategy is implemented using the measured stickout as feedback to adjust deposition feedrate. Results show that the control system successfully compensates for improper layer height increments, enabling thin-wall builds to consistently reach target geometry.

Machines doi: 10.3390/machines14050532

Authors: Francisco Espartero Javier Cubas Santiago Pindado

The gradual increase in man-made objects in the space surrounding our planet is becoming increasingly evident. This significant rise in terrestrial materials is reflected in a greater presence of artificial satellites, space debris and waste from space missions. These objects orbiting close to Earth pose a significant risk in the event of uncontrolled re-entry, as well as collisions between the artificial satellites themselves, launch vehicles and space stations in Earth orbit. This article presents the experimental progress achieved during the prototype phase of a new model of robotic satellite observatory (SRO), featuring significant advances in its design and capabilities. These new SROs are intended to have dual capability to operate simultaneously in both scientific and military contexts. The possibility of forming a network with these devices will provide a system that substantially improves orbital determination and the identification of space objects of interest. The result presented here is an advanced model of the SRO, featuring substantial design improvements from both an ergonomic and economic perspective, as well as a significant enhancement in its ability to monitor and track space objects of uncertain origin that may be of interest or considered a threat to security, thereby expanding its Space Situational Awareness (SSA).

Machines doi: 10.3390/machines14050533

Authors: Xiang Mo Song Zheng Yeqi Guan Zhezhuang Xu Ping Huang

Accurate segmentation of shoe upper processing boundaries is crucial for automated trajectory generation and high-precision robotic control. However, developing a robust method is challenging due to the frequent style changes in High-Mix Low-Volume production. The reliance on large-scale annotated datasets renders traditional supervised methods impractical due to the prohibitive cost of annotation and retraining. To address these issues, a multimodal-based point cloud segmentation strategy is proposed for shoe upper processing boundaries. First, an unsupervised adaptive local spectral contrast filtering algorithm is designed to remove large amounts of background noise and isolate potential target regions by exploiting boundary color characteristics. Then, an unsupervised dynamic ellipsoidal neighborhood color-spatial region growing algorithm is developed based on geometric features of slender and closed boundary shapes to suppress interferences flanking the boundaries. Finally, a Siamese network is designed to perform few-shot matching against boundary templates exported from Shoemaster, effectively decoupling intrinsic boundary signals from complex extrinsic interferences to achieve precise segmentation. Experimental results demonstrate that the proposed method achieves a stable mean Intersection over Union (mIoU) of approximately 0.80. Compared to existing supervised and unsupervised baselines, this strategy exhibits superior generalization across diverse styles and effectively resolves the data dependency bottleneck.

Machines doi: 10.3390/machines14050531

Authors: Yani Liu Tongli Ren Meiling Jiang Li Zhang Tingting Liu

Substantial progress has been made in bearing-fault classification under same-condition settings, yet cross-condition diagnosis remains affected by three coupled issues: speed-induced temporal-scale perturbation, insufficient use of structured label information, and domain shift across operating conditions. To address these issues, this paper presents BearingPRO, a unified framework that combines physics-guided representation alignment with group ordinal labeling for bearing-fault classification. The framework contains three modules. First, a TimeWarp module applies controlled temporal stretching and compression to emulate waveform variations induced by speed changes. Second, a Grouped Ordinal module introduces intra-group ordinal constraints according to the hierarchical relation between fault type and fault severity. Third, a Physics-Guided Representation Alignment (PGRA) module uses rotational-speed priors for carrier-frequency calibration, envelope extraction, and cross-domain alignment. On the CWRU bearing dataset, under the 0 HP → 3 HP transfer task, BearingPRO achieves 0.9205 ± 0.0088 accuracy and 0.8962 ± 0.0105 Macro-F1 in the unified reproduction setting used in this study. Relative to the re-implemented comparison methods under the same backbone and training budget, the proposed framework yields higher mean performance and lower variance. Ablation results further indicate that temporal-scale modeling, grouped ordinal supervision, and physics-guided alignment play complementary roles in the current setting. At the same time, the scope of the conclusion is explicitly bounded: the present evidence is obtained on the CWRU test rig, within the considered speed range, and under artificially introduced point defects; therefore, the method is presented as a well-supported cross-condition classifier for this benchmark, not as a universally validated solution for all motors.

Machines doi: 10.3390/machines14050530

Authors: Wasiq Ullah Mehroz Fatima Mohammad A. Abido Udochukwu B. Akuru Husam S. Samkari Mohammed F. Allehyani Abdul Khalique Junejo

Due to the widespread adoption of high-performance electric vehicles (EVs), Interior Permanent Magnet (IPM) machines have achieved significant advancement in the field of electric motors due to their high torque density and efficiency. However, research has been ongoing for many decades to suppress the rare-earth permanent magnet (PM) usage without sacrificing electromagnetic performance while still achieving the required torque, power, and efficiency. In this regard, various EV manufacturers, such as Honda, Toyota, Chevrolet, BMW, and Nissan, have developed different types of IPM topologies; however, the rare-earth PM usage is extensively high, and the torque density is lower. Thus, to reduce the PM consumption and improve the electromagnetic performance, especially torque density, this paper proposes a novel segmented delta-shaped IPM (SΔ-IPM) with a three-notched rotor pole shape having two different specifications and featuring embedded circular flux barriers and an intermediate flux bridge. Secondly, torque performance is analytically discussed, and electromagnetic performance has been evaluated using 2D finite element analysis (FEA). Due to its unique design featuring improved magnetic field shifting, an average torque of 393.7 Nm with torque ripples of 5.1% and a cogging torque of 0.57 Nm has been achieved. Finally, an extensive comparative analysis of the aforementioned ten state-of-the-art industry models has been conducted, which confirms the effectiveness of the proposed design for high torque density with minimum PM usage.

Machines doi: 10.3390/machines14050529

Authors: Wojciech Kacalak Katarzyna Tandecka Zbigniew Budniak Thomas G. Mathia

This study describes the design and analysis of a dual-roller superfinishing attachment for the abrasive-film microfinishing process, where two independently mounted compliant conical rollers make contact with separate zones of the abrasive film in order to manage the geometry of the contact zone. In this regard, the geometry-related simulation based on a 3D SolidWorks 2022 model was carried out to analyze the effects of the vertical shift of the contact-zone center point, h = 1–4 mm, and horizontal deformation of the abrasive film, δx = 0.1–0.5 mm. Under each setting, the contact area, Ac, the geometric interference volume, Vint, and the characteristic contact-zone length, lc, were evaluated. Increasing the value of δx from 0.1 mm to 0.5 mm resulted in the growth of the average Ac by 2.27 times, increasing from 79.42 mm2 to 180.41 mm2, and Vint by 11.4 times, growing from 5.26 mm3 to 59.94 mm3. The effect of h on Ac is relatively small, which means that h mostly affects the positioning of the contact zone, and δx controls its size and geometric interactions.

Machines doi: 10.3390/machines14050528

Authors: Wei Han Xianlei Shan Tong Tong Haitao Liu Juliang Xiao

The selection and design of core functional components are a primary task in the engineering design of heavy-duty industrial robots, in which joint constraint forces and moments act as essential indicators for component selection. This paper proposes a general inverse rigid-body dynamics model for serial kinematic chains that explicitly incorporates joint constraint forces and moments. On this basis, a rigid-body dynamics model for heavy-duty industrial robots is established, which fully considers inertia, gravity, and balancing forces of the balance system, and is verified through dynamic simulations. Corresponding selection and design criteria are then formulated for joint motors, RV reducers, and balance systems. Simulation analyses and prototype full-load tests jointly confirm that the robot meets the 1000 kg load capacity requirement and validate the effectiveness of the proposed selection and design criteria. This study provides a reliable theoretical and engineering reference for the design and development of heavy-duty industrial robots.

Machines doi: 10.3390/machines14050527

Authors: Xulong Zhang Hong Jiang Dong Han Hai Guo Xiangfeng Zhang Kaige Sun

In open environments, lightweight pedestrian and vehicle detection models deployed on edge platforms of Unmanned Ground Vehicles (UGVs) often struggle to balance detection accuracy and inference efficiency when facing complex backgrounds, distant small targets, and occluded objects. To address this, we propose a lightweight object detection model based on YOLO11n, named UGV-Net. This model enhances feature interaction and global context modeling capabilities by introducing the C3k2_PS module, employs Dysample dynamic upsampling to achieve content-aware feature reconstruction, and designs an LSDECD detection head to reduce multi-scale prediction redundancy and computational overhead, thereby balancing detection accuracy and inference efficiency. Compared with the baseline model YOLO11n, UGV-Net improves F1 score, mAP50, and mAP50:95 by 2.19%, 2.26%, and 2.10%, respectively, on the KITTI dataset, while reducing GFLOPs from 6.3 to 4.9 and the number of parameters from 2.58M to 2.41M. Similarly, on the SODA10M and FLIR datasets, the F1 score improves by 1.71% and 1.41%, and mAP50 improves by 1.67% and 2.39%, respectively, demonstrating excellent detection accuracy and generalization ability. Furthermore, experiments on the Jetson Orin Nano platform verify that UGV-Net achieves robust real-time detection performance, making it an efficient, reliable, and lightweight solution for UGV perception in open environments.

Machines doi: 10.3390/machines14050526

Authors: Tianxiu Wang Shikun Zhang Yuzhuan Bao Zhen Fu Wenzhi Gao Jing Zhang

Hybrid transmission, as the power core of hybrid vehicles, has a whining problem which affects the driving experience seriously. It is of great engineering value to carry out Noise, Vibration, and Harshness (NVH) research. In this paper, a combined methodology of finite element simulation, multi-body dynamics analysis, and real-vehicle experiment is adopted to improve the whine of the hybrid transmission. Firstly, a finite element model of the DHT assembly is established, with the frequency deviation between modal simulation and test being less than 5%, meeting the accuracy requirements. Through real-vehicle tests in electric vehicle (EV) mode, the 8th and 24th orders are identified as the key whine orders, and the deviation between simulation and test for the noise of these relevant orders is ≤5 dB(A). The research clarifies that the coupling resonance between the local modes of the upper and lower cover plates of the DHT and the excitations of the P3 motor is the core mechanism leading to the whine, and the motor control unit (MCU) is confirmed as the main noise emission source. Notably, the weak structural stiffness of the MCU lower cover plate is the critical inducing factor. To address this, three support blocks are added at the center of the MCU lower cover plate for structural reinforcement. After optimization, the 8th-order vibration is reduced by an average of approximately 35 dB in the speed range above 3500 rpm, and the 24th-order vibration is decreased by an average of about 20 dB within the range of 1000–1500 rpm. Specifically, the 24th-order noise near 1300 rpm is reduced by around 13 dB, and the 8th-order noise above 3500 rpm is fully suppressed. The increasing trend of noise with rising speed is significantly curbed, and the overall NVH performance of the vehicle is greatly improved.

Machines doi: 10.3390/machines14050525

Authors: Tiantian Chen Junqing Li Li Wei Junchao He

In this study, a special version of the Flexible Job Shop Scheduling Problem with equally and consistently batching constraints (hereafter called ECBFJSP) is considered, which involves multiple aspects of coordination, such as machine selection, process sorting, and batch splitting, which is highly complex and places strict demands on the optimization strategy. To effectively meet this challenge, this study constructs a dual-action deep reinforcement learning algorithm framework based on the Enhanced Heterogeneous Graph Neural Network (EHGNN). First, an enhanced heterogeneous graph and EHGNN model for the ECBFJSP is innovatively proposed. By integrating multi-dimensional node features such as work order priority, machine tool processing capability, and process constraints, dynamic feature aggregation of various types of information is achieved with the help of GATs and GRUs. The model can output context-aware representations containing global resource constraints, greatly improving the joint optimization efficiency of job scheduling and batch partitioning and significantly enhancing the adaptability of the dual-action decision framework to the complexity of the ECBFJSP. At the decision-making mechanism level, this study designed a dual-action decision space of process sequencing–machine selection action and batch partitioning action and used the DAPPO algorithm to collaboratively optimize the dual-action strategy to ensure the stability and efficiency of the decision-making process. The experimental data results show that compared with traditional algorithms, the proposed intelligent decision framework performs better in scheduling quality when solving the ECBFJSP, which fully verifies the significant effectiveness and practicality of the framework in solving the ECBFJSP.

Machines doi: 10.3390/machines14050524

Authors: Paolo Righettini Roberto Strada Filippo Cortinovis

Pneumatic actuators are widely used components for industrial automation, primarily due to their high power-to-volume ratio, high speed, simplicity of use, and the ease with which they can satisfy applications’ motion requirements. The accurate evaluation of the energy consumption in pneumatic systems is a topic of increasing importance due to contemporary attention to sustainability. Actuators of the same size have different pneumatic energy needs depending on the specific function they must accomplish, in terms of displacement, force, operational speed, and motion time. The dedicated literature focuses either on the best practices for the configuration of the circuit or on the pneumatic energy consumption in specific sections of the system; when electrical energy consumption is treated, only steady-state conditions are considered. Starting from this state of the art, this paper proposes a new general procedure for the assessment of the electrical energy required to perform specific functions in industrial machinery that relies on pneumatic actuators. The procedure is based on the evaluation of the air mass consumption of the actuator considering its actual application requirements, either through experimental measurements performed on the machinery or through a numerical simulation of the system. Regarding the numerical approach, an experimentally validated dynamic multi-domain model of the pneumatic system is proposed to also properly analyse the relevant transient behaviour. Starting from the evaluation of the air mass consumption, the procedure outlines how to calculate the corresponding electrical energy requirements, considering the application requirements and the operating point of the compressor. To this end, the procedure also includes a method to tailor the ISO 1217 standard to the actual working conditions of the compressor. The procedure, which enables the accurate mapping of the requirements placed by the application on the pneumatic actuator to the ultimately required electrical energy inputs, is validated through several simulations of real cyclic application cases. The results in terms of electrical energy consumption are compared to those obtained with the traditional method based on the steady-state behaviour of the system; the comparison shows that the proposed procedure evaluates the energy consumption with greater accuracy in applications characterized by highly dynamic work cycles.

Machines doi: 10.3390/machines14050523

Authors: José Vicente Roig Julián Salt

In autonomous vehicle trajectory tracking, lane-keeping control often relies on high-order robust controllers designed with multiple performance requirements encoded via weighting functions. Such designs typically entail a significant computational cost that can overload processors already devoted to demanding tasks, such as computer vision. This work introduces an interlacing strategy for the implementation of a given robust state-space controller, with the aim of reducing its computational burden while preserving acceptable closed-loop behavior. The approach operates directly on a state-space realization and is extendable to high-order MIMO controllers, considering both diagonal (modal) and balanced realizations combined with different input–output update schemes. The method is illustrated through simulations under conditions representative of an automotive test-track circuit, indicating that substantial computational savings can be achieved at the expense of a moderate deterioration of the closed-loop response.

Machines doi: 10.3390/machines14050522

Authors: Chunlin Peng Xiaowei Yuan Fei Wang Jiachun Sun Shaochen Yang Yakun Zhang

The Earth Pressure Balance (EPB) shield machine plays a pivotal role in underground tunnel excavation, where precise control of chamber pressure is essential for maintaining tunnel stability and minimizing risks. Traditional EPB control methods heavily rely on operator experience, resulting in delays and limited responsiveness to sudden geological changes. This paper presents an improved EPB mechanism model that builds upon traditional approaches, which primarily consider chamber pressure changes caused by soil volume variations. The improved model further incorporates the effects of excavation face pressure variations, arising from factors such as cutterhead soil extrusion and changing geological conditions. By integrating these additional influences, the model achieves more accurate predictions of chamber pressure. To further enhance performance, a hybrid modeling approach is proposed, combining the improved mechanism model with a data-driven component that compensates for residual prediction errors. The hybrid model is validated using field data from two distinct tunneling projects, demonstrating superior prediction accuracy and generalization capability compared to standalone mechanisms and data-driven models. The results confirm that the proposed hybrid model significantly improves pressure prediction accuracy and provides a more reliable solution for intelligent control of the EPB process.

Machines doi: 10.3390/machines14050520

Authors: Jinwei Qiao Xue Wang Shoujian Yu Na Liu Shasha Zhou Zhenyu Li Rongmin Zhang

Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field.

Machines doi: 10.3390/machines14050521

Authors: Fuga Inagaki Yuki Sadasue Masami Iwase

The purpose of this study is to develop a robot that reduces labor and automates cable-laying work at construction sites. The robot should have the ability to pull lead cables over cable racks and ceiling spaces. Therefore, we propose a reconfigured active-cord-mechanism robot based on the RSAW mechanism that can move in both environments by maintaining continuous traveling wave propagation across multiple units connected through joints. A prototype robot was first constructed to verify the applicability of the RSAW mechanism to cable-laying environments. However, the discontinuity of the traveling wave at the joint connections prevented the prototype from traversing ceiling spaces. Based on this finding, a new robot was developed with a configuration that ensures continuous wave propagation across the joints through mechanical design and phase synchronization control. As a result, the new robot enhances propulsion speed and cable traction. Additionally, the robot can move over ceiling joint receivers that exist in ceiling spaces. Comparative analysis with previous prototype robots and a snake-like robot highlights this robot’s advantages, including reduced motor count, autonomous operation with mounted power and control units, and superior turning capabilities.

Machines doi: 10.3390/machines14050519

Authors: Hao Jiang Laibin Zhang Jianchun Fan Yanran Wang Yilin Fang Kaiwen Wang Yingying Ye

Drill pipe threads are susceptible to fatigue cracking under complex downhole loads, posing significant risks to drilling safety. Although metal magnetic memory (MMM) testing enables efficient nondestructive evaluation, its practical utility is often compromised by interference from material magnetization and lift-off distance. To overcome this limitation, we introduce a novel damage assessment method based on the area peak-to-mean ratio (APMR) of magnetic signals. This feature is specifically designed to suppress external disturbances while retaining sensitivity to stress-induced magnetic anomalies. Finite element analysis was performed to elucidate the magneto-mechanical coupling behavior at defective thread roots. Subsequently, MMM signals were acquired from drill pipe threads under varying inspection conditions using a custom-built scanning system equipped with 16 tunneling magnetoresistance (TMR) sensors. Multiple features, including APMR, were extracted and evaluated across various machine learning classifiers. The gradient boosting machine (GBM) achieved superior performance, yielding an accuracy of 0.9861 and a recall of 1.0000 on the test set—outperforming all other models. This work presents an effective automated approach for drill pipe thread damage evaluation, contributing to enhanced reliability and safety in drilling operations.

Machines doi: 10.3390/machines14050518

Authors: Xijie Song Zhengwei Wang Huili Bi Lianheng Guo Daqing Qin Yongxin Liu

This study investigates how different needle openings govern the coupled evolution of sediment erosion and entropy production-based hydraulic dissipation in a Pelton turbine. Three representative needle openings (20%, 40%, and 54%) are examined by CFD simulation, and the total entropy production is decomposed into wall entropy production, direct dissipation, and indirect dissipation to quantify the opening-dependent irreversibility budget. The results show that the transition zone and buckets consistently dominate the total entropy production, accounting for 84.48%, 80.78%, and 81.57% of the total at 20%, 40%, and 54% openings, respectively, indicating that the nozzle–runner interaction region is the principal carrier of irreversible loss. Meanwhile, reduced opening intensifies jet contraction and promotes non-uniform sediment redistribution, whereas larger openings improve jet coherence and enhance particle flow-following behavior. The wall-level results further reveal that the correspondence between erosion rate density and wall entropy production becomes progressively more evident with increased opening, especially on the bucket pressure side, while particle incidence statistics indicate a transition from broader-angle, locally triggered impacts at small openings to predominantly grazing delivery at larger openings. Overall, the results demonstrate that needle opening does not merely change the magnitude of loss or erosion, but systematically reorganizes the coupled pathway of jet development, sediment redistribution, near-wall dissipation, and wall damage in a Pelton turbine.

Machines doi: 10.3390/machines14050517

Authors: Lijuan Zhao Mingyang Mao Kun Sun Qiang Liu Jin Qin

The performance bottleneck in high-rotational-speed turbomolecular pumps (TMPs), wherein a significant increase in rotational speed does not lead to a noticeable increase in pumping speed, can be attributed to a mismatch between the traditional straight blade structure (TSBS) and high rotational speeds. Therefore, based on the theory of molecular gas dynamics, a gas molecular transport model incorporating twisted stator blade rows (TSBRs) was established. Utilizing the Monte Carlo (MC) method, a simulation calculation program was developed and validated in previous research. Taking structural parameters of the first four stages of a TMP available in the laboratory as an example, research on the necessity of TSBRs in TMPs was conducted. Our research findings indicate that increasing the probability of collisions between gas molecules and the lower surface is beneficial to improve pumping speed. Gas molecules incident on stator blade rows are no longer evenly distributed, exhibiting a trend of being sparse in the middle and dense at the edges. The maximum pumping speed coefficient and maximum compression ratio of the four-stage combined blade rows with TSBRs increased by 71.86% and 15.14%, respectively. Our research findings confirm the necessity of incorporating TSBRs and also provide direction and theoretical guidance for the structural optimization of TMPs.

Machines doi: 10.3390/machines14050516

Authors: Yujie Shi Masato Mizukami Naohiko Hanajima Yoshinori Fujihira

Inspection inside small-diameter pipelines is difficult because the narrow interior space limits the field of view of onboard cameras. Even when a crack is successfully detected, it may still appear near the image boundary rather than in a suitable position for observation. To address this issue, this study proposes a vision-guided active crack alignment framework for small-diameter pipe inspection robots. The proposed framework uses a YOLOv5s detector to identify the crack region and extract the center of the detected bounding box. The positional difference between the crack center and the image center is defined as the image-plane alignment error. After low-pass filtering, this error is converted into actuator-side reference input through a pixel-to-motor mapping, and a PID-based closed-loop controller is used to regulate a local camera adjustment mechanism so that the detected crack region moves toward the image center. The framework is evaluated mainly through simulation, including controller comparison, different initial offset conditions, parameter sensitivity analysis, robustness tests under visual fluctuation and mapping uncertainty, and an ablation study. The controller comparison shows that all tested PID-based controllers achieve stable convergence, while the fuzzy PID controller provides the best overall performance among the tested cases in terms of settling time, steady-state error, and RMS error. The framework also remains stable under different crack positions and moderate uncertainty conditions. In addition, a preliminary laboratory-scale physical consistency test is conducted to examine whether the convergence tendency observed in simulation can also be reproduced under real visual feedback and actuator response. The preliminary physical results show a convergence tendency consistent with the simulation trend, thereby providing initial support for the practical implementability of the proposed detection-driven alignment concept. Complete integration with an in-pipe robot platform and validation under realistic pipe environments remain future work.

Machines doi: 10.3390/machines14050515

Authors: Veselina Krasimirova Dimitrova Ventsislav Panev Dimitrov Galya Stoyanova Zdravcheva

This study presents an analytical–experimental investigation of the mechanical and tribological behaviour of two coating systems applied to deep, internally profiled cylindrical components manufactured via Electrochemical Rifling (ECR): a hard anodised aluminium oxide (AAO) coating on an aluminium alloy and a hard chromium coating on alloy steel. Experimental characterisation includes microhardness measurements, coefficient of friction determination, and controlled sliding wear tests. The chromium coating exhibits approximately 2.5 times higher microhardness and about 15% lower average coefficient of friction compared to the anodised aluminium layer, resulting in significantly improved wear resistance. Acceptable engineering agreement is observed between analytical predictions and experimental results. For chromium-coated steel, analytical predictions yield approximately 67,200–72,600 cycles, while the experimentally estimated value is about 36,200 cycles. For anodised aluminium, analytical predictions range from approximately 1688 to 2803 cycles, compared to an experimental value of about 2012 cycles. A conservative reliability-oriented criterion yields service lives of approximately 12,000 cycles for chromium coatings and 1000 cycles for anodised aluminium. Weibull-based analysis (R = 0.95) indicates service life ranges of approximately 9300–10,000 and 230–390 cycles, respectively.

Machines doi: 10.3390/machines14050514

Authors: Tianlong Wang Xinwei Wang Shihao Hu Shixuan Yang Zhaohui Cai Buqiao Fan Tong Xiang Muhammad Moman Shahzad

Concrete vibration quality has an outsized effect on structural durability, but construction sites have no reliable way to monitor it in real time. Compounding this, vibration machinery has no self-awareness of its own operating state, so failures and degradation tend to go unnoticed until something goes wrong. The proposed system integrates a Raspberry Pi controller and a hybrid neural network model within the vibrator apparatus itself. The model pairs a 1D CNN with a Kolmogorov–Arnold Network (KAN). The CNN initially conducts the majority of the computational workload: it systematically reduces dimensionality and extracts salient features from extensive time-series data, thereby circumventing the convergence challenges that a KAN encounters when processing unrefined high-dimensional sequences independently. Subsequently, a B-spline-based classification module supersedes the conventional fully connected layer. This innovation is noteworthy; the module is capable of identifying minute damping variations and frequency alterations during the process of concrete liquefaction, accurately distinguishing between states such as “adequate compaction” and “over-vibration,” which may appear nearly indistinguishable in their dynamic responses. The achieved accuracy in vibration state classification was 97.55%, while recognition of no-load conditions reached 98.17%. The system provides millisecond-level active protection against hazardous impacts, effectively reducing equipment wear. With a low implementation cost of approximately 800 RMB and a projected 20% improvement in construction compliance, this work provides reliable technical support for ensuring controllable construction quality and extending equipment service life, offering an efficient solution for the intelligent upgrade of building equipment.

Machines doi: 10.3390/machines14050513

Authors: Kah Yin Goh Ming Foong Soong Rahizar Ramli Ahmad Saifizul

This paper investigates the performance of a speed-dependent variable inerter in improving vehicle suspension performance. Unlike conventional and passive inerter suspensions with fixed mechanical properties, the proposed speed-dependent variable inerter allows continuous adjustment of inertance according to the relative acceleration between the sprung and unsprung masses, enabling variable inertance under changing driving speeds and road conditions. A quarter-vehicle model is employed to evaluate a conventional passive inerter and both a linearly and non-linearly increasing variable inerter system in series and parallel layouts. A multi-objective genetic algorithm simultaneously optimizes the suspension damping and variable inertance range with respect to ride comfort and road-holding ability. To further validate the simulations, the optimized systems are evaluated under step, random and sinusoidal road profiles. The results showed that a linearly increasing variable inerter, particularly in parallel configuration, offers the best compromise between ride comfort and road holding, achieving up to 4.94% improvement in ride comfort under a random road profile, outperforming conventional passive inerter and non-linearly increasing inerter suspensions, while maintaining acceptable tire–road contact. Performance improvements under step and sinusoidal road profiles were moderate, while more significant performance gains were observed under a random road profile due to the larger acceleration change induced, which led to larger inertance variation. These findings confirmed the potential of variable inerters as an alternative approach to vehicle suspension systems, due to their passive implementation, absence of control requirement and compatibility with compact suspension architectures.

Machines doi: 10.3390/machines14050512

Authors: Garrett Madison Grayson Michael Griser Gage Truelson Braden Churches Christopher Lee Colaw Yildirim Hurmuzlu

Material delivery plays a critical role in manufacturing efficiency, with manual retrieval introducing non-value-added (NVA) time and disrupting workflow continuity. Autonomous mobile robots (AMRs) can improve performance by enabling overlap between material transport and productive work, but their effectiveness depends on how they are deployed. In this work, a convolutional neural network (CNN)-based autonomous dispatch framework was implemented and tested in a controlled experimental setting. This study utilized a representative aerospace assembly task to evaluate three material delivery approaches across 60 runs, including manual walking, manual AMR dispatch, and autonomous AMR deployment. System performance was assessed using total operation time, panel lead times, and non-value-added time. Results showed that manual AMR dispatch significantly increased total operation time and non-value-added time due to sequential task execution. Autonomous deployment reduced this inefficiency by enabling preemptive material transport and overlap with operator activity, but did not significantly outperform manual walking under the tested conditions. Operator variability also influenced non-value-added time under automated dispatch. These results indicate that AMR effectiveness depends strongly on deployment timing and workflow synchronization, with the greatest potential benefits expected in environments that allow greater overlap between transport and productive work.

Machines doi: 10.3390/machines14050511

Authors: Hui Guo Boqiang Shi Hu Chen Haoran Zhu Bingbing Liu

This study addresses the high-precision position control problem of pilot-operated proportional directional valves under dead-zone nonlinearity. A fuzzy PID-based position control strategy optimized by the dung beetle optimizer (DBO-FPID) is proposed to alleviate switching lag and accuracy degradation caused by dead-zone effects. First, a refined nonlinear model combining theoretical analysis and AMESim simulation is established to quantitatively characterize the dead-zone evolution mechanism of the valve system, and the dead-zone range of the directional valve is identified as ±34.5% of the duty cycle. On this basis, a multiphysics co-simulation model is developed to analyze the static and dynamic characteristics of the pilot valve and the main spool. Then, the DBO algorithm is introduced to optimize the key parameters of the fuzzy PID controller by minimizing an objective function based on the integral of time-weighted absolute error (ITAE), thereby improving the controller’s compensation capability for dead-zone nonlinearity. Simulation results show that, compared with DBO-PID, the proposed DBO-FPID control strategy reduces the rise time by 54.4%. During triangular and sinusoidal position tracking, the dead-zone residence time is reduced by 47.5% and 44.8%, respectively, while the mean absolute error remains below 0.2 mm. Experiments further validate the effectiveness of the proposed control strategy for high-precision position control of the pilot-operated proportional directional valve.

Machines doi: 10.3390/machines14050510

Authors: Bowen Cha Jun Luo Bin Xu Zilong Guo

Triboelectric nanogenerators (TENGs) exhibit great application potential in the fields of intelligent sensing and Internet of Things terminal devices due to their advantages of self-powering, simple structure, and high sensitivity. A self-powered alarm sensor for smart manhole covers is proposed to realize real-time monitoring of water immersion and abnormal displacement without external power supply. Experimental results show that the sensor can generate distinguishable voltage signals under water immersion and different displacement states, enabling rapid recognition of potential hazards such as manhole cover offset and accumulated water. On this basis, a reliable intelligent alarm system is constructed, which can receive, analyze, and warn of abnormal signals in real time. Therefore, it can even directly replace commercial manhole covers, demonstrating the broad application prospects of TENG in the field of intelligent monitoring. With the continuous advancement of TENG technology, the functions of this manhole cover alarm will be further expanded and optimized in the future, providing stronger support for the construction of smart cities.

Machines doi: 10.3390/machines14050509

Authors: Guangxian Li Luyang Ding Meng Liu Hui Xie Songlin Ding

Femtosecond laser ablation (FLA) is efficient for the machining of micro-groove arrays on the surface of ultrahard cutting tools. The depth of the groove determines the precision and efficiency of ablation. In this study, an “Attention-based Monotonic Physics-Guided Neural Network” (AM-PGNN) algorithm is proposed to accurately predict groove depth in the FLA of tungsten carbide (WC). The new algorithm incorporates machining parameters directly governing the energy deposition and thermal accumulation, thereby determining the prediction of the micro-groove depth generation. By embedding the physics-guided monotonic relationships of parameter depth into the learning process, a dedicated physical loss coupled with an attention mechanism to enable adaptive feature weighting is constructed, which strengthens the representation of causal dependencies. Experimental data for training and testing are obtained from the FLA of WC with different machining parameters. Comparison between AM-PGNN and typical algorithms, including a Support Vector Machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Gradient Boosting Decision Tree (GBDT), and a conventional PGNN, demonstrates that the proposed AM-PGNN achieves superior prediction accuracy. Moreover, AM-PGNN attains a physical consistency degree (PCD) of 100%, indicating strict adherence to monotonicity consistent with the actual situation. AM-PGNN also exhibits enhanced robustness to input perturbations, as reflected by reduced standard deviation (Std) and normalized absolute deviation (NAD). Finally, AM-PGNN is shown to be applicable in the FLA of different materials through additional experiments on Cu and SiC, achieving R2 values above 0.93 while maintaining a PCD of 100%.

Machines doi: 10.3390/machines14050508

Authors: Xinfeng Zou Haiyan Miao Baoshan Huang Zhen Li Fengshou Gu

To monitor online the operational condition and quality of a desktop fused deposition modeling (FDM) printer, the dynamics of vibro-acoustics must be accurately understood. In this paper, an electromechanical coupling (EMT) framework is established to relate the dynamics of stepper actuation, the transmission chain, and machine motion, deriving a steady-state frequency–velocity mapping for steady or near steady printing segments. The mapping is evaluated by numerical calculation to obtain a theoretical drive frequency for different toolpath directions and commanded printing velocities. Validation is performed on the experiment platform I. Drive-side vibration is measured by an accelerometer mounted on the x-axis beam near the motor end. An acoustic channel is recorded as an auxiliary qualitative cross-check rather than for quantitative error evaluation. For steady printing segments, the dominant frequency in drive-side vibration is compared with the theoretical drive frequency. In the tested steady segments and toolpath directions, the relative error remained below 3%. In a further case study, the G-code is modified to introduce two constant printing velocity segments (40 mm/s and 80 mm/s) within the same continuous record, enabling a direct comparison of dominant frequencies between two steady segments. The results show that, under open-loop stepper drive and within the steady/near steady scope adopted here, a drive-related dominant frequency can be observed stably in the x-axis beam vibration response and matches the theoretical drive frequency. When the commanded constant printing velocity is doubled, the dominant frequency in drive-side vibration in the corresponding steady segment changes by approximately a proportional factor. This study provides a verifiable drive referenced frequency–velocity mapping for steady segments under the tested configuration and a traceable frequency reference for steady segment comparisons within the same print record in subsequent case studies.

Machines doi: 10.3390/machines14050507

Authors: Tarkan Koca Mehmet Bilal Er Aydın Çıtlak

Early detection of bearing faults in rotating machinery is essential for ensuring system reliability and effective maintenance planning. Vibration signals inherently contain characteristic fault-related frequency components, providing rich information for both physically interpretable and data-driven analyses. In this study, a multi-representation and correlation-aware feature extraction framework is proposed for automatic classification of bearing faults from vibration signals. Experimental evaluations are conducted using the Case Western Reserve University (CWRU) Bearing Dataset. The dataset includes vibration recordings corresponding to inner race, outer race, ball faults, and healthy conditions under different damage severities. The proposed approach first applies Variational Mode Decomposition (VMD) to suppress noise and enhance frequency-related characteristics. Three different feature representations are then constructed: analytical spectral descriptors, raw Transformer-based deep representations, and a hybrid feature vector obtained by combining these two representations. The hybrid structure is further enhanced through regularized Canonical Correlation Analysis (rCCA), which models the relationship between Transformer representations and spectral descriptors, enabling correlation-aware feature fusion. Spectral, raw Transformer, and rCCA-enhanced hybrid feature vectors are evaluated separately using SVM, Random Forest, and XGBoost classifiers. The results demonstrate that both spectral and Transformer-based representations provide strong performance individually; however, integrating these complementary information sources while modeling their correlations leads to superior and more balanced classification performance. In particular, the rCCA-enhanced hybrid feature vector achieves the best results across all performance metrics. The findings indicate that combining physically meaningful frequency-domain information with data-driven deep representations yields a more robust and generalizable solution for bearing fault diagnosis.

Machines doi: 10.3390/machines14050506

Authors: José Alarcón Christine Rousselle Ignacio Calderón Magdalena Walczak Wolfram Jahn

In recent years, strong emphasis has been put on decarbonising the transport and mining sectors in an economically viable manner. To this end, ammonia is presented as a fuel, combining a high energy density (when compared to hydrogen) and zero carbon emissions. In this work, conversion of a mining haul truck engine is simulated for its use with an ammonia/diesel dual-fuel system at up to 70% Ammonia Energy Replacement (AER). The numerical setup is partially validated against engine performance data. The simulations suggest a reduction in CO2 emissions but an increase in N2O, which increases the carbon-equivalent emissions of the engine. Nevertheless, NOx emissions appear to be reduced, suggesting the use of post-treatment is required to deal with the issue of N2O. Cylinder temperature control is recommended for its reduction, as temperatures are lower when burning ammonia. On the other hand, the simulations suggest that ammonia slip increases with AER if diesel injection phasing is not optimised. Performance-wise, the engine develops a higher indicated mean effective pressure (IMEP) as AER increases, with a maximum at 40% AER, while combustion is delayed progressively into the engine cycle, as CAD50 values increase from −0.6 CAD ATDC at 0% AER to 20.1 CAD ATDC at 70% AER. Opportunities for further research are discussed, including more extensive experimental work to support or reject what is suggested by the simulations.

Machines doi: 10.3390/machines14050505

Authors: Carlos Mafla-Yépez Jorge Melo Paul Hernández Cristina Castejón Diego Teran-Pineda

In the framework of Agriculture 4.0, the modernization and predictive maintenance of legacy heavy machinery are essential for ensuring food security and operational efficiency. This study presents a non-invasive automated diagnostic system for classifying the operational status of mechanical diesel injection pumps in agricultural tractors through vibration analysis and machine learning. A rigorous experimental setup was conducted on an International 523 tractor to acquire vibration signals under controlled fuel pressure conditions ranging from 1 to 4 bar, with 2 bar established as the optimal nominal pressure. The signal processing methodology employed a hybrid feature extraction approach, integrating spectral components from the Fast Fourier Transform (FFT) with time-domain statistical variables. After evaluating 33 classification algorithms, a Support Vector Machine (SVM) model demonstrated superior performance, achieving a training accuracy of 96.7% and Area Under the Curve (AUC) values exceeding 0.90 across all classes. Notably, the model achieved perfect identification (AUC = 1.0) of critical low-pressure faults (1 bar), which significantly compromise engine start-up and combustion efficiency. Validation with an independent dataset confirmed the robustness of the system, maintaining a 95% accuracy rate. These findings validate the proposed approach as a reliable, low-cost solution for condition monitoring, facilitating the integration of conventional tractors into digital maintenance ecosystems.

Machines doi: 10.3390/machines14050504

Authors: Jiahao Song Yiyi Zhao Yuxian Zhang Keyao Lian Shuofei Yang

Resolving inverse kinematics and evaluating constrained workspace remain challenging for multi-module cable-driven robots. This paper analyzes a dual-module cable-driven hybrid robot inspired by inchworm locomotion and proposes a new configuration-based decoupling strategy in which a scalar decoupling factor relates the dominant bending variables of the two modules and organizes the redundant inverse kinematics into C-shaped and S-shaped configuration sets. A physically constrained workspace evaluation framework that combines the decoupling rule with cable-stroke, unilateral-cable, joint-deflection, and anti-winding constraints is established. Monte Carlo sampling is used to extract the admissible position and orientation workspaces. The results show that the dual-module topology increases the lateral reach from 65.2 mm to 180.6 mm relative to a single module while retaining a yaw range of about ±80°. In addition, the workspace subsets associated with positive and negative values of the decoupling factor correspond to boundary-reaching motion and lateral body adjustment, respectively. These results show that the proposed decoupling parameterization provides a valid way to organize redundant configurations and to evaluate the workspace amplification introduced by serial coupling.

Machines doi: 10.3390/machines14050503

Authors: Paolo Righettini Roberto Strada Filippo Cortinovis Jasmine Santinelli Federico Tabaldi

Energy efficiency and sustainability are core issues in the modern design and management of industrial machinery and plants. These concerns are reflected and reinforced by the Sustainable Development Goal 9 of the United Nations (SDG9), “Industry, innovation and infrastructure”, which enshrines efficiency and optimized energy use as key features of sustainable production systems. As the engineering of industrial machinery reorients itself towards energy sustainability, attention is naturally shifting to actuators, since these components unavoidably waste part of the considerable amount of energy they absorb to execute their functions. Hydraulic actuation systems, while uniquely suited to heavy-duty applications, are particularly affected by poor energy conversion efficiency, in part due to their intrinsic properties but also because of outdated yet still common industrial practices. Consequently, for this actuation technology, there are wide margins for improvement in terms of energy waste reduction and increased environmental sustainability. This paper, therefore, investigates new applications for a management and control method conceived by the authors to drastically and systematically reduce the energy consumption of hydraulic actuators. The method is easily retrofittable to existing plants, being based on the unconventional and non-invasive deployment of a continuous-control electrohydraulic valve (CCEV) to control the supply pressure, whose required value is estimated according to the instantaneous load demands. Through the simulation of several industrial processes characterized by process parameters of varying orders of magnitude, this paper demonstrates that this innovative use of a CCEV for supply pressure regulation is an effective and widely applicable solution for energy savings and CO2 footprint reduction in production systems that rely on hydraulic servo axes.

Machines doi: 10.3390/machines14050502

Authors: Fusheng Wang Yongliang Wang

To address the issues of sealing leakage and airflow-induced vibration in high-speed turbomachinery, a conical straight-tooth annular clearance sealed hybrid aerostatic/aerodynamic bearing is investigated. A three-dimensional CFD model is established to study the effects of radial clearance height, inlet pressure, rotor speed, and eccentricity on pressure distribution, velocity distribution, and leakage rate. The results show that leakage exhibits a strong positive nonlinear correlation with clearance height and inlet pressure, following a power-law or polynomial relationship, while rotor speed and eccentricity exert negligible effects (less than 5%). The underlying mechanisms are identified as the kinetic energy diversion caused by circumferential shear and the mutual cancelation of throttling and backflow effects. Increasing the gap height enhances leakage by expanding the hydraulic diameter and strengthening vortex disturbance; increasing inlet pressure promotes leakage by elevating the driving force and intensifying local flow separation.

Machines doi: 10.3390/machines14050501

Authors: Xiangye Zhu Qi Liu Liang Liang Xiaohu Xu Sijie Yan

The instantaneous contact zone in robotic abrasive belt grinding involves highly coupled thermo-mechanical interactions between abrasive grains and the workpiece material. Acoustic Emission (AE) signals generated during this process are inherently nonlinear and nonstationary, posing challenges for accurate process monitoring and mechanistic understanding. To address this, this study introduces an innovative AE signal processing framework designed to elucidate the robotic grinding mechanism for Ti-6Al-4V (TC4) titanium alloy. An improved Completely Complementary Ensemble Empirical Mode Decomposition (CCEEMD) algorithm, building upon Empirical Mode Decomposition (EMD), is developed to precisely extract intrinsic mode functions (IMFs) from raw AE data. Subsequently, a novel denoising algorithm utilizing noise statistical characteristics effectively removes invalid noise from the robotic machining system. Validation through robotic grinding experiments on TC4 workpieces successfully established quantifiable relationships between extracted AE features and the underlying grinding mechanism. Significantly, implementing this methodology contributed to extending the effective service life of a structured abrasive belt by approximately 20% while increasing machining efficiency by approximately 12%. This work presents a novel methodology combining improved CCEEMD and statistical denoising for AE analysis in robotic grinding, providing a robust link between AE signatures and material removal mechanisms, ultimately enabling quantitative process optimization.

Machines doi: 10.3390/machines14050500

Authors: Miao Liu Shuguang Han

Multi-objective flexible job shop scheduling requires balancing conflicting objectives while supporting real-time decision-making in industrial environments. However, although traditional metaheuristics are effective for global search, their high computational cost limits their applicability in time-sensitive scenarios. To address this issue, this paper proposes dual-pool guided preference-conditioned graph reinforcement learning (DPG-GRL), an encoder–decoder framework for the multi-objective flexible job shop scheduling problem. In DPG-GRL, a graph attention network encoder extracts operation and machine-level representations from a heterogeneous graph, while the decoder is conditioned on a preference vector to generate scheduling solutions with different trade-offs using a single trained policy. To improve sample efficiency and training stability, a dual-pool guidance mechanism is introduced, in which an offline expert pool provides a stable behavioral prior for policy initialization and an online elite pool continuously replays high-quality trajectories to refine the policy. Experimental results show that DPG-GRL outperforms representative multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D), on synthetic instances, with more pronounced advantages in solution quality and inference efficiency as the problem scale grows. In addition, evaluations on public benchmark instances using a model trained only on the small synthetic setting demonstrate rapid Pareto-front approximation, high-quality solution sets, and promising generalization to unseen instances. These results indicate the potential of DPG-GRL for real-time production scheduling and energy-aware manufacturing.

Machines doi: 10.3390/machines14050499

Authors: Sergey Dobrotvorskiy Yevheniia Basova Borys A. Aleksenko Dmytro Trubin Mikołaj Kościński Paweł Zawadzki Marcel Lojka Michal Hatala

The study is devoted to determining the degree of influence of the first and second stages of AISI 321 steel surface treatment with a femtosecond laser, with unchanged laser parameter characteristics, on the blackening parameters and parameters characterizing the distribution of surface heights according to the ISO 25178 standard. Surface blackening is important for production automation for better visibility of markers by optical sensors. The assessment is carried out from the point of view of changing the degree of blackening of the studied surface. During the experiment, it was found that secondary surface treatment without changing the processing mode leads to an insignificant, up to 5%, increase in the degree of surface blackening. Secondary laser processing revealed a diminishing returns effect, where doubling the energy input in perpendicular scanning resulted in only a marginal (~5%) gain in blackening. This phenomenon stems from surface morphological saturation, as the primary roughness parameters Sq and Sdq attain their plateau values, without contributing to the further formation of hierarchical light-trapping structures. It was also found that during the blackening process, such parameters as the maximum peak height Sp, the ten-point surface height S10z, and the asymmetry Ssk increased more than others. After repeated treatment, the values of the parameters maximum valley depth Sv and the root mean square slope Sdq increased the most. At the same time, the nature of the normal Gaussian surface height distribution was preserved. As the Sdq value increases, the number of randomly located reflecting surfaces increases, and this leads to better scattering of the directed beam. On the other hand, the absence of random fragments on the vertices of the periodic surface structure, which corresponds to a high Sku index, allows such a surface to scatter light more effectively.

Machines doi: 10.3390/machines14050498

Authors: Andrea Petrotto Lorenzo Franchi Giuseppe Mattei Luca Pugi

Maneuverability and performance of UAVs are strongly influenced by the adopted propulsion layout. Electrification has enabled modern UAVs to achieve unprecedented maneuverability, including hovering and VTOL (Vertical Take Off and Landing) capabilities, allowing the adoption of complex propulsion layouts otherwise impossible to manage with conventional fossil powered machines. Despite significant advancements in lithium-based cell technologies, the energy densities achieved by current storage systems remain insufficient to ensure extended operational autonomy. Hybrid systems represent an effective compromise, combining the high energy density of conventional fuels with agile power management of electric storage systems. In this work, the authors investigate the design, modelling, and control of an innovative autonomous compound helicopter equipped with a hybrid propulsion system. For this purpose, a comprehensive digital twin has been developed, capable of simulating the interactions among the vehicle, propulsion system, and energy management systems under a predefined mission profile.

Machines doi: 10.3390/machines14050497

Authors: Martin Bilka Igor Gritsuk Andrii Holovan Olena Volska Iryna Honcharuk Marcel Kohutiar Michal Krbata

This study develops a physics-informed digital twin framework for quasi-real-time assessment and forecasting of marine plate heat exchanger performance under variable environmental and operational conditions. Unlike conventional steady-state or purely data-driven approaches, the proposed framework integrates first-principles thermohydraulic modeling, an iterative successive-approximation solver, and continuous synchronization with operational ship data, enabling adaptive state estimation and degradation tracking. The methodology explicitly accounts for coupled thermal, hydraulic, and fouling processes, and incorporates uncertainty-aware validation under real ship operating conditions. A case study based on a central cooling system of a cargo vessel demonstrates that seawater temperature variations of 3–4 K can induce nonlinear system responses, including up to a fourfold increase in coolant demand, a 10–15% reduction in heat-transfer efficiency, and a 15–25% rise in hydraulic losses. A threshold operating regime is identified, characterized by rapid degradation and fouling amplification. Comparative analysis against a static baseline model shows that the digital twin improves predictive accuracy and enables early detection of performance deterioration. Energy-efficiency assessment indicates that adaptive cooling control supported by the digital twin can reduce auxiliary power demand and contribute to fuel savings. The proposed framework provides a scalable foundation for predictive maintenance and intelligent thermal management in maritime systems.

Machines doi: 10.3390/machines14050496

Authors: Xuliang Lu Lin Zhang Dong Wei

The intelligent level of hydraulic support directly impacts the safe mining capacity and efficiency of the entire fully mechanized mining face. Adaptive intelligent control of its supporting pose, to adapt to the complex and ever-changing geological conditions of coal seams and variations in roof characteristics, is crucial for intelligent mining. This paper proposes and validates a novel adaptive adjustment method for the supporting pose, utilizing hydraulic support dynamics and transfer reinforcement learning. First, the supporting dynamics based on the coupling relationship between the hydraulic support and the surrounding rock of the coal seam are analyzed, and a supporting reinforcement learning model based on the Markov Decision Process is designed. Based on this model, a gradient-optimized Proximal Policy Optimization method is proposed, and a virtual dynamic simulator is built for training a supporting pose control policy. To transfer the virtually trained strategy to real hydraulic supports for practical application and to bridge the gap between support strategy simulation and reality, a progressive neural network architecture is introduced to mitigate the execution gap of support strategies under real-world conditions. Experimental results demonstrate that the proposed method can effectively and autonomously adjust the supporting pose to adapt to changes in the complex and variable coal seam roof. Furthermore, this work provides a theoretical foundation and practical engineering application for the development of intelligent support robots in coal mines.

Machines doi: 10.3390/machines14050495

Authors: Jun Liu Ziyan Yang Xinfu Xu Tianhang Zhou Yazhou Zhou

To satisfy the stringent functional-safety requirements of steer-by-wire steering systems for advanced autonomous driving, this paper proposes a novel dual-motor collaborative fault-tolerant control strategy. The proposed approach aims to overcome the insufficient fault tolerance of conventional single-motor architectures, as well as the limited dynamic response and disturbance-rejection capability observed in existing multi-motor schemes. The key contribution is an integrated control framework consisting of two components: (i) dual-motor torque synchronization achieved via a fuzzy-PID–based mean-deviation coupling method, and (ii) a super-spiral sliding-mode control law optimized by an adaptive differential-evolution algorithm to enhance the dynamic performance and robustness of the current loop. Experimental results demonstrate that, relative to a non-synchronized baseline, the proposed strategy reduces the inter-motor current mismatch by 8.1–78.6% across multiple operating conditions. Moreover, following fault occurrence, the proposed Self-Adaptive Differential-Evolution-algorithm-based Super-Twisting Sliding-Mode Control method shortens the stabilization time by 50–70%, 9–20%, and 16.7% compared with conventional PID, Super-Twisting Sliding-Mode Control methods, and classical H∞ robust control, respectively. Overall, the developed solution meets functional-safety requirements and provides a highly reliable steering-actuation mechanism for advanced autonomous driving applications.

Machines doi: 10.3390/machines14050494

Authors: Guoqing Hu Tonglin Song Jian Xue

The cantilever pick-and-place arm of the high-speed placement machine is susceptible to micro-vibration and elastic deformation under high-acceleration motion, thereby degrading chip placement accuracy. To address this issue, this paper presents an analytical study on the natural frequency characteristics and structural optimization of slender variable-cross-section rods. First, based on the thin-walled shell theory, a displacement field model of the thin-walled cantilever rod is established. Second, combining the energy method and Hamilton’s principle, the undamped free vibration equation is derived. Using the Rayleigh–Ritz method with Chebyshev polynomials as the basis functions, an analytical calculation model for the natural frequency of the variable-section thin-walled rod is constructed. The model is validated against finite element simulations, and the relative errors of the low-order natural frequencies are controlled within 5%, confirming its favorable accuracy and robustness. Furthermore, the four-factor three-level orthogonal experiment is designed with the objective of maximizing natural frequency to conduct parameters sensitivity analysis. Accordingly, the optimal structural parameter combination ϕ3 = 8 mm, L1 = 10 mm, L2 = 50 mm, and L3 = 5 mm) is determined. Finally, the maximum dynamic deformation under high-acceleration motion decreases from 0.066 mm to 0.021 mm, a reduction of 68.2%, which effectively suppresses residual vibration and end displacement deviation. The analytical method proposed in this study provides a theoretical basis for the rapid dynamic performance evaluation of flexible components in high-speed precision equipment, and the optimization strategy can offer engineering references for the high-stiffness design of key components in chip placement machines.

Machines doi: 10.3390/machines14050493

Authors: Peilin Zheng Xu Zheng Yi Qiu

Sound field reproduction (SFR) is vital for noise simulation and acoustic comfort optimization in vehicle cabins. This paper reviews three core SFR techniques: Wave Field Synthesis (WFS), Higher-Order Ambisonics (HOA), and Pressure Matching (PM). Their theoretical fundamentals, engineering optimizations, and adaptability to narrow enclosed cabins are analyzed. We compare the three methods in terms of reproduction accuracy, system complexity, and cost. Key challenges in vehicular applications are summarized, including strong reverberation, multi-source coupling, and the mismatch between physical reproduction and subjective perception. Future directions are proposed, such as physics-data hybrid optimization, low-cost lightweight design, and personalized acoustic comfort. This review offers a practical reference for the engineering application of SFR in vehicle cabin acoustic optimization.

Machines doi: 10.3390/machines14050492

Authors: Mingyuan Wang Yize Xu Ziqing Gu Jianjun Yuan Sheng Bao Zhengtao Hu

To improve the adaptability and adhesion of wall-climbing robots on complex curved surfaces, a self-adaptive spherical magnetic wheel robot is proposed for inspecting three-dimensional variable-curvature structures. The robot employs a bilateral wheeled design with passive magnetic modules that automatically adjust to contact conditions, ensuring efficient adhesion without active control. A Halbach-array magnetic circuit further enhances adhesion without increasing size or weight. Simulations analyze the effect of swing angle on adhesion and determine the minimum adaptable curvature radius. Experiments show stable climbing on surfaces with radii of 100–350 mm, obstacle-crossing up to 7 mm, and a payload capacity of 16.63 kg. Compared with existing designs, the robot offers improved curvature adaptability and load capacity under similar size and weight constraints.

Machines doi: 10.3390/machines14050491

Authors: Ahmed Al Saadi Rahizar Ramli Ahmad Saifizul Sudhir Chitrapady Vishweshwara

Heavy industrial vehicles operating in aluminum smelters are exposed to severe thermal, mechanical, and environmental stresses, which increase the likelihood of failure and unplanned downtime. This study proposes an Economic Risk Priority Number (ERPN) framework to address the limitations of the conventional Risk Priority Number (RPN) used in Failure Mode and Effects Analysis (FMEA). A five-year maintenance dataset (2019–2024), comprising 2303 corrective work orders from 58 heavy equipment units, was analyzed. The classical RPN approach prioritized failure modes mainly according to occurrence and detectability, identifying the wheel and hydraulic subsystems as the most critical. In contrast, the proposed ERPN framework integrates economic impact through maintenance cost, manpower cost, and production loss, resulting in the engine subsystem being ranked as the most critical. The most severe engine failure caused an estimated financial loss of approximately USD 1.92 million due to extended downtime and repair costs. Root cause analysis identified coolant loss, low oil pressure, and excessive vibration as the main contributors to catastrophic engine failure, supported by diagnostic evidence and repeated alarm patterns. Statistical validation performed using the Kruskal–Wallis test confirmed significant differences among subsystem risk distributions for both RPN (χ2 = 846.07, df = 4, p < 0.0001) and ERPN (χ2 = 131.69, df = 4, p < 0.0001). The findings demonstrate that ERPN provides a more economically meaningful framework for maintenance prioritization and offers a practical decision-support tool for reducing operational risk in aluminum smelter fleets.

Machines doi: 10.3390/machines14050490

Authors: Abdullahi Abubakar Christian Klumpner Patrick Wheeler

This paper proposes a low weight hybrid battery–supercapacitor energy storage system interfaced with bidirectional DC/DC converters with high power/current capability for aircraft applications. The supercapacitor converter having high power uses two pairs of interleaved coupled inductors to reduce the overall current ripple whilst increasing the converter’s power density. Due to the sensitive performance to saturation of the coupled inductors, a phase current balancing strategy is proposed to counter the effect current imbalance in the channels that would cause saturation degrading overall performance. A power management strategy (PMS) is implemented along with a low pass filter to separate the supercapacitor high frequency power component reference from the battery low frequency power component; therefore, separating the energy and power requirement for the energy storage system contributing to minimizing its weight whilst ensuring the current/power stresses are correctly handled. The validity of the system design is validated by a series of transient tests is conducted both in a simulation model as well as experimentally.

Machines doi: 10.3390/machines14050489

Authors: Syed Hassan Imam Saqib Jamshed Rind Saba Javed Mohsin Jamil

The requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems.

Machines doi: 10.3390/machines14050488

Authors: Randyll Pandohie Edgard M. Maboudou-Tchao Nihad Habizada Morris Beato Aman Behal

Understanding how users perceive assistive robotic systems is critical for their successful adoption, particularly in rehabilitation settings where both patients and clinicians influence decision-making. While prior work has focused on technical performance and overall usability, affective responses such as trust, control, and perceived independence are often captured using coarse, single-score measures that overlook important nuances. This study analyzes focus group discussions with individuals with spinal cord injury to examine how users evaluate different aspects of assistive robot design. A hybrid aspect-based sentiment analysis approach is applied, combining lexicon-based and transformer-based methods to capture both interpretable and context-sensitive sentiment. The analysis separates sentiment across key dimensions, including independence, functionality, safety, control, cost, and data sharing. Participants expressed consistently positive views toward independence and functional support, while responses related to safety, control, and data sharing were more conditional. In particular, trust emerged as something that depends on transparency, user control, and the ability to override system behavior, rather than a fixed attitude toward the technology. These findings suggest that successful assistive robotic systems must balance autonomy with user authority and provide clear, adaptable mechanisms for control and data governance.

Machines doi: 10.3390/machines14050487

Authors: Andrew Clark Mason Currens Nathan Kowalczyk Brian Rath Anthony Quear Jadyn Towns Ananth Murthy Sang-Eun Song

Robotic technologies are increasingly investigated for craniofacial and dental surgical procedures where sub-millimeter positional accuracy and stable instrument trajectories are essential. This structured review evaluates the current landscape of robotic systems applied to craniofacial surgical interventions and analyzes their technical architectures, validation approaches, and reported surgical accuracy. A structured literature search of PubMed and IEEE Xplore identified 27 studies published between 2015 and 2025 that met predefined inclusion criteria. The included systems were analyzed with respect to robotic control architecture, surgical application domain, validation model, and quantitative performance metrics. To facilitate cross-study interpretation, the review introduces a unified engineering classification framework linking robotic control paradigms, mechanical configurations, and clinical application domains. Most platforms employed master–slave teleoperation, image-guided hybrid control, task-autonomous execution, or cooperative haptic-guided architectures designed to stabilize surgical trajectories and reduce surgeon-dependent variability. Across representative investigations, robotic systems demonstrated entry-point deviations typically ranging from approximately 0.6–1.5 mm and angular deviations between 1.2° and 3.5°, indicating improved reproducibility compared with conventional freehand techniques. Dental implant robotics currently represents the most clinically mature application, whereas sinus, skull base, and microsurgical systems remain largely in experimental or early translational stages. Overall, craniofacial surgical robotics demonstrates substantial potential to enhance surgical precision and procedural standardization; however, broader clinical validation and improved workflow integration remain necessary for widespread clinical adoption.

Machines doi: 10.3390/machines14050486

Authors: Jasper Zevering Joshua Braun Martin Hesse Kedus Mathewos Dorit Borrmann Anton Bredenbeck Andreas Nüchter

The exploration of lunar caves is a critical aspect of the space exploration program of the European Space Agency (ESA). To facilitate this mission, the DAEDALUS study investigated a novel spherical robot design in 2021. The proposed robot uses a unique telescopic linear rod mechanism to generate rotation and hence locomotion. This drive mechanism requires a dedicated control scheme to ensure both locomotion and simultaneously stabilization of the robot. The overall task of following a curved trajectory is also a problem that cannot be solved by simple algorithms. In this work, we introduce, calculate, and simulate a solution for these tasks, the Virtual Pose Instruction Plane (VPIP). The VPIP breaks the problem of multiple independent controllable rods down to two controllable parameters (roll and pitch of the plane), which control the linear motion velocity, balance and ultimately curvature motion of the robot. Initial simulations show that both speed and cornering can be controlled by the VPIP.

Machines doi: 10.3390/machines14050485

Authors: Aftab Ali Samejo Jianfei Chen Yigang He Andres Annuk Imad Hussain Adeel Bashir

To address the performance requirements of power interface converters in fuel cell vehicles, a high-voltage gain DC–DC converter with a common-ground structure and zero-voltage-transition (ZVT) operation is proposed. The converter employs two interleaved boost cells in an input-parallel output-series (IPOS) configuration to achieve low input-current ripple and high voltage gain. A single auxiliary circuit enables soft-switching for all switches during turn-on and turn-off, while diodes operate under zero-current switching (ZCS), reducing switching and reverse-recovery losses. In addition, voltage stress across devices is limited to half of the output voltage, allowing the use of lower-rated components. A 1 kW prototype operating at 50 V input and 400 V output at 50 kHz is experimentally validated. The converter achieves efficiency above 92.5%, with a peak of 95.46% and up to 97.41% at higher input voltages, while maintaining stable output performance. These results demonstrate the suitability of the proposed converter for high-efficiency fuel cell-based applications.

Machines doi: 10.3390/machines14050483

Authors: Jiří Sika Michal Křížek Tomáš Kavalír Bohumil Skala

This study addresses the premature failure of electric motor bearings caused by inverter-induced parasitic currents. We propose a novel segmented end shield design utilizing 24 carbon fiber-reinforced polymer (CFRP) lamellae to provide both structural support and galvanic isolation. The “main working” of the design relies on a segmented architecture where the lamellae are adhesively bonded between a central bearing housing and an outer mounting flange, creating a high-impedance path that interrupts circulating currents. Experimental validation focused on both mechanical stability and dielectric performance. Results indicate that the assembly maintains rotor positional integrity under nominal loads while providing an insulation resistance > 1 GΩ at 1 kV and a structural capacitance of 2.47 nF. These parameters effectively mitigate low-frequency circulating currents. Data analysis, derived from the mean values of repeated test cycles, confirms that the composite architecture serves as a viable, mechanically robust alternative to conventional metallic end shields.

Machines doi: 10.3390/machines14050484

Authors: Giovanni Gerardo Muscolo Michele Conconi Alessandra Del Felice Lorenzo Chiari Nicola Sancisi

Balance loss in older adults often leads to severe consequences, and wearable exoskeletons may help restore postural stability. This paper presents a novel hip cable-driven exoskeleton designed to support balance recovery. The proposed postural control strategy implemented on the device is based on maintaining balance by reducing the center of mass displacement from its equilibrium condition. Loss of balance is analyzed using multibody human models both with and without the exoskeleton. Simulation results evaluating static and dynamic balance demonstrate the effectiveness of the proposed control strategy and support its feasibility for implementation in a real system. The simulations presented in this study will be compared with experimental results from human subjects in future work.

Machines doi: 10.3390/machines14050482

Authors: Qibo Wang Tiancheng Li Jinyuan Tang Zhou Sun

In the aerospace industry, thin-web gears are preferred for achieving high power-density transmission. However, thin-webbed structures always lead to out-of-plane resonance during the transmission process, which commonly happens in helical gears, manifesting as severe vibration at a specific rotational speed. To address this, a shaft–web–ring dynamic model is proposed. The shaft, gear web, and gear ring are modelled based on the Timoshenko straight beam, Mindlin plate, and Timoshenko bent beam theory. Simultaneously, the potential energy caused by the time-varying meshing stiffness is coupled to the gear ring. The kinetic and potential energies of each discretized finite element of the components are derived based on elastic deformation theory, and the governing equations of each element are obtained using Hamilton’s principle. The model is verified through a modal experiment. The comparison with traditional rotor-gear models has demonstrated the significance of gear body flexibility in helical gears with thin webs. The effects of the web thickness and helix angle on dynamic response are studied, revealing that gear web elasticity and an appropriately high helix angle can effectively reduce vibrations at the support bearing, prevent excessive vibrations, and contribute to vibration and noise reduction in the transmission system.

Machines doi: 10.3390/machines14050481

Authors: Chao-Chung Liu Ming-Nan Chen Chao-Shu Liu

This study presents the design, simulation, and experimental validation of a novel sector-shaped thread rolling machine aimed at improving forming efficiency, structural compactness, and process controllability compared with conventional linear thread rolling systems. A systematic engineering framework integrating mechanism design, curved-die implementation, motion control, finite-element simulation, and experimental verification is established. DEFORM-3D simulations are performed to investigate the effects of friction coefficient and die spacing on material flow and thread profile formation, and the results are used to guide machine construction and parameter optimization. Experimental results demonstrate that the proposed mechanism can simultaneously form four screws within a single rotation cycle, significantly enhancing production efficiency. Under optimized parameters, the relative errors of pitch diameter and helix angle are maintained within 5%, showing good agreement with simulation predictions. The findings confirm the feasibility, controllability, and stable forming capability of the proposed system, providing a practical and efficient solution for next-generation compact and high-productivity thread rolling equipment.

Machines doi: 10.3390/machines14050480

Authors: Euicheol Shin Seohee Jang Seongwan Kim Chan Roh Heemoon Kim Jongsu Kim Daehong Lee Hyeonmin Jeon

Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems.

Machines doi: 10.3390/machines14050479

Authors: Qinqin Mu Qun Yan Chao Hang Yonghui Chen

To elucidate the damping mechanism of platform dry friction dampers for turbine blades and optimize their design parameters, this study establishes a two-dimensional global–local unified sliding dry friction damping model. This model comprehensively accounts for the blade’s bending-torsion coupling vibration characteristics and the dual-state behavior of the damper, encompassing both stick and slip phases. An iterative solution strategy combining finite element methods with in-house developed programs is employed to simulate the vibration response of turbine blades equipped with dampers under multiple loading conditions. The influence of normal pressure and dimensionless normal pressure on the blade’s vibration characteristics, equivalent stiffness, and equivalent damping is systematically analyzed. To validate the reliability of the simulation results, a dedicated test platform capable of independently simulating centrifugal force effects was constructed, and modal tests as well as vibration response tests were conducted. The results demonstrate that the proposed model accurately describes the nonlinear energy dissipation behavior of dry friction damping, providing a reliable theoretical basis for blade vibration response analysis. Dimensionless normal pressure is identified as a key parameter influencing vibration reduction effectiveness. The resonant amplitude of the blade exhibits a non-monotonic trend, initially decreasing and then increasing with rising dimensionless normal pressure. The optimal dimensionless normal pressure range is found to be 20–30, within which the blade vibration amplitude can be reduced by more than 50%. Experimental verification confirms that the vibration reduction and energy dissipation mechanism of the damping block aligns closely with simulation results, achieving a maximum vibration reduction of 72.6%. Moreover, the optimal dimensionless normal pressure values correspond well with simulation predictions. Based on the optimal dimensionless normal pressure, a forward design method for platform dampers is proposed, which can provide theoretical support and engineering guidance for the optimal design of vibration reduction structures in aero-engine turbine blades.

Machines doi: 10.3390/machines14050478

Authors: Yaofei Han Xiaodong Qiao Zhiyong Huang Shaofeng Chen Yawei Li Bo Yang

The torque ripples of robotic permanent magnet synchronous motors (PMSMs) degrade motion smoothness and positioning accuracy of the system, while inevitable load transients in robotic tasks further complicate torque ripple attenuation. To address this issue, this paper develops an event-triggered torque ripple attenuation method that explicitly distinguishes torque ripple from dynamic load transients. First, a sliding-mode torque observer is constructed to obtain real-time torque information, whose stability is rigorously analyzed using a Lyapunov function. Second, frequency-selective torque ripple extraction schemes are proposed to accurately isolate steady-state high-frequency torque ripple from the estimated torque signal. In particular, two specially designed filtering structures are developed and compared, one of which is selected to preserve ripple-related frequency content during test, ensuring robust and accurate ripple identification under varying operating conditions in robotics. Third, a torque-ripple-regulation-based compensation strategy is used within a vector-controlled PMSM drive, in which the extracted torque ripple is processed by a dedicated ripple regulator to generate voltage compensation signals. This strategy achieves effective steady-state torque ripple attenuation with low implementation complexity, while avoiding performance degradation during dynamic load transients. Finally, experimental results are provided to validate the effectiveness of the proposed methods.

Machines doi: 10.3390/machines14050477

Authors: Néstor Alcañiz-Brull Pau Varela Pedro Quintero Roberto Navarro

The transition toward renewable energy sources has positioned wind energy as a critical technology for achieving global carbon neutrality targets. While large-scale wind farms dominate current installations, micro-scale horizontal-axis wind turbines present significant potential for distributed energy generation in remote and rural areas. This study presents a comprehensive methodology for designing micro-scale wind turbine blades through comparative analysis of three computational approaches: classical blade element momentum theory (BEMT), QBlade 2.0.9.6 software, and Computational Fluid Dynamics (CFD) simulations, with the design methodology selected based on a trade-off between accuracy and computational cost. A numerical campaign for airfoil assessment was conducted to identify optimal blade geometries, with performance evaluated based on power coefficient distribution, peak power output, and cut-in wind speed. The investigation reveals that steady CFD simulations predict peak power coefficients 23.34% higher than those predicted by BEMT and 22.46% higher than those predicted by QBlade due to three-dimensional effects, including rotational stall delay. Considering unsteady effects, the CFD simulations show a decrease of 4.08% with respect to steady simulations. The addition of endplates to the optimized blade design demonstrates significant performance improvements. This multi-fidelity approach provides a robust framework for micro-scale wind turbine design, balancing computational efficiency with accuracy requirements, and examines the impact of adding endplates.

Machines doi: 10.3390/machines14050476

Authors: Liyuan Yu Jitao Fang Qiuyan Wang Fajia Li Haining Liu

Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems.

Machines doi: 10.3390/machines14050475

Authors: Like Pan Tong Xing Wenrui Dai Yan Xu Weidong Zhu

The dynamic characteristics of the collector shoe–conductor rail interaction directly affect the operational performance of metro systems. Although irregularities exist in both the track and the conductor rail, their relative influence on interaction dynamics has not been comprehensively compared. This study develops a coupled train–track–shoe–rail dynamic model to investigate these effects. Specifically, the conductor rail is modeled using a localized approach based on the Arbitrary Lagrangian–Eulerian (ALE) method. This is integrated with a multibody collector shoe model and an existing train–track interaction model to form a comprehensive simulation framework. After validating the model, the impacts of track irregularity, conductor rail irregularity, and support spacing are analyzed and compared. The results demonstrate that while conductor rail irregularity is the primary driver of contact loss, standard track irregularity can also account for approximately 10% of the dynamic response variation. Consequently, both factors must be integrated into future studies of collector shoe–conductor rail dynamics.

Machines doi: 10.3390/machines14050474

Authors: Yixiang Chen Menghao Ran Shiyun Lin Yuhuan Du

This study achieves a 30.8% mass reduction in amphibious UAV rotary arms via additive manufacturing-constrained topology optimization (AM-constrained TO), establishing lightweight design as the primary objective. To evaluate structural efficiency, we systematically compare three strategies: AM-constrained TO, Hexagonal Honeycomb infill (HC), and central lightening holes (ES). All configurations target comparable mass reduction. Using the SIMP method with manufacturing constraints, TO designs were generated. FEA and tensile tests evaluated stiffness, strength, failure modes, and Specific Energy Absorption (SEA). The key innovation lies in the TO approach: It achieves the primary objective of 30.8% mass reduction while simultaneously enhancing structural integrity and outperforming HC, ES, and Solid Baseline (SB) configurations in stiffness (2234 ± 76 MPa), Specific Energy Absorption (742 ± 29 J/m3), and stress distribution uniformity. The HC configuration shows progressive collapse but has the lowest stiffness (886 ± 17 MPa) and SEA (432 ± 5 J/m3) due to FDM inter-layer bonding limits. The ES configuration has the second-highest tensile strength (19.489 ± 0.19 MPa), but stress concentration around the hole reduces energy absorption, resulting in lower SEA (620 ± 15 J/m3) than TO.

Machines doi: 10.3390/machines14050473

Authors: Changyu Yue Boming Liu Bokai Liu Liwen Guan

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates C2-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry.

Machines doi: 10.3390/machines14050472

Authors: Yongze Zhang Feipeng Da Haocheng Zhou

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance.

Machines doi: 10.3390/machines14050471

Authors: Ju Zhou Lin Wang Tao Wang

In high-value precision machining, existing tool wear monitoring models often suffer from two major limitations: poor generalization under varying cutting conditions and heavy reliance on large amounts of labeled data for new operating scenarios. These limitations hinder the practical deployment of intelligent monitoring systems. To address these challenges, this paper proposes a few-shot warm-start framework based on model-agnostic meta-learning. The method consists of two stages. First, meta-training is performed on historical machining data to learn a task-sensitive parameter initialization that enables rapid adaptation. Second, under a new operating condition, the few-shot warm-start mechanism collects a minimal number (1 to 5) of samples through a targeted physical trial-cutting process for online fine-tuning, aligning the model with the current physical environment. Experiments on the PHM2010 dataset fully simulate varying cutting scenarios. The experimental results demonstrate that the proposed framework consistently outperforms traditional transfer learning, deep learning models, and existing meta-learning approaches, offering an effective solution for fast and accurate tool wear prediction under few-shot and varying cutting conditions.

Machines doi: 10.3390/machines14050470

Authors: Yongjie Li Binbin Li Yu Zhang

Motor fault diagnosis under small-sample conditions remains challenging because limited labeled data often cause deep models to overfit and generalize poorly. To address this problem, we propose STR-DDPM, a fault data augmentation framework that combines moving-average-based seasonal–trend–residual decomposition with a denoising diffusion probabilistic model. Specifically, multichannel signals are decomposed into trend, seasonal, and residual components, and class-conditional diffusion modeling is performed only in the residual domain. This design emphasizes fault-related stochastic variations while reducing interference from deterministic structures. To improve generation stability, we adopt velocity prediction and develop an enhanced one-dimensional U-Net with multi-scale convolutions, channel attention, self-attention, and feature-wise linear modulation for controllable conditional generation. Experiments on the University of Ottawa and Paderborn motor fault datasets demonstrate that the proposed method generates samples that are highly consistent with real data and improves diagnostic performance under multiple synthetic-data-assisted settings. These results indicate that STR-DDPM provides an effective and practical solution for data augmentation in data-limited motor fault diagnosis.

Machines doi: 10.3390/machines14050469

Authors: Hjalte Durocher Christian Bachmann Rocco Mozzillo Günter Janeschitz Xuping Zhang

Future fusion power plants like DEMO must be remotely maintained for safety, including breeding blankets (BBs) weighing up to 180 t. The BB vertical transporter (BBVT), a crane-like redundant robot with 7 joints, has been previously designed for handling the five unique BB segments per sector. This includes grasping, preloading and collision-free spatial manipulation of BB segments in a space-constrained environment, necessitating advanced motion planning and real-time control. To achieve this, the challenge of obtaining accurate and performant inverse kinematic (IK) solutions for the redundant BBVT must be addressed. Therefore, a kinematic model is presented, and the redundant IK probelm is solved analytically for task-relevant cases, including derivation and analysis of the Jacobian. The model is verified by comparison with an MSC Adams model. Meanwhile, the analytical IK is found to be 53× to 84× faster than a gradient projection-based numerical solver in Matlab while providing multiple solutions. The IK and Jacobian are applied to create collision-free waypoints, verified in Matlab, for handling each BB segment while minimizing static joint loads in key configurations. A first-order estimate of the total BB handling time for a maintenance of nine days is calculated. These developments support the feasibility of the BBVT robot for the BB maintenance task in DEMO, and underpin future efforts in modelling dynamics and achieving real-time resilient control.

Machines doi: 10.3390/machines14050468

Authors: Junjie Zou Kun Tang Fengjun Chen Wentao Wang Yuanqiang Luo Weidong Tang Cong Mao Yongle Hu

Ultra-high-speed cutting (UHSC) has emerged as a transformative manufacturing technology aimed at overcoming the long-standing machining challenges associated with high-performance difficult-to-machine composites (HPDMCs). These materials—comprising silicon-based, metal matrix, and carbon fiber-reinforced polymers—are critical to strategic sectors such as aerospace and high-end equipment. This review adopts a distinctive “material-tool-process-equipment” synergistic innovation framework as its core analytical lens. Within this framework, it systematically outlines advances in UHSC, including the fundamental mechanisms of damage suppression and surface integrity enhancement under ultra-high strain rates. Innovative process methods such as laser-assisted and ultrasonic-assisted machining are examined in detail. This review also provides a mechanistic analysis of two key enabling technologies—tool micro-texturing and functional coatings—highlighting their roles in interfacial tribological regulation and physicochemical protection. Furthermore, dedicated equipment systems and stability optimization strategies essential for technological implementation are presented and evaluated. By synthesizing the current state of the field, this review identifies persistent bottlenecks and, guided by the proposed framework, suggests targeted future research directions: deep integration of smart manufacturing technologies, development of synergistic multi-energy-field processing, and enhanced adaptability to extreme service environments. This work not only consolidates the current knowledge in UHSC but also outlines a clear pathway for its evolution into a fully autonomous, efficient, and reliable manufacturing paradigm.

Machines doi: 10.3390/machines14050467

Authors: Vinoth Kumar Shriram Srinivasarangan Rangarajan Chandan Kumar Shiva E. Randolph Collins Tomonobu Senjyu

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks.

Machines doi: 10.3390/machines14050466

Authors: David Sommer Abdulrahman Safi Cemal Esen Ralf Hellmann

We report on tool wear and surface roughness for hybrid additive manufacturing of Inconel 718 components. The hybrid additive manufacturing comprises laser powder bed fusion (PBF-LB/M) and an in situ high-speed milling process, i.e., milling is performed within the powderbed, which deteriorates the surface quality by additionally occurring wear mechanisms. Therefore, in this comparative study milling path suction is used to improve tool wear characteristics and thus enhance surface quality. As a result, we quantify the improvement of the maximum tool life according to the flank wear, which is granted by the milling path suction. Additionally, the dominant wear mechanisms are investigated, revealing adherence and abrasion as the main contributing factors to wear. Furthermore, surface analysis shows an improvement of surface quality by the use of the milling path suction. Specifically, a reduction in surface roughness of hybrid manufactured Inconel 718 components down to a minimum of Ra = 0.55 μm is highlighted.

Machines doi: 10.3390/machines14050465

Authors: Caixin Fu Changhong Jiang Zhiwei Wan Peng Cheng Shenquan Wang

This article addresses the fault detection (FD) problem for traction drive systems in the presence of unknown noise covariances. The dynamic behavior of the traction drive system, affected by actuator and sensor faults, is first formulated. Following the philosophy of the subspace identification, the system matrices are identified directly from collected process data using QR decomposition and singular value decomposition. Based on the identified model, a robust Kalman filter (KF)-based FD scheme is developed. By exploiting the iterative interaction between the estimator and measurement data within the KF framework, the noise covariance matrices are adaptively estimated, which alleviates the adverse effects caused by empirical covariance selection in conventional KF-based FD methods. Experimental results obtained from a real traction drive system verify the effectiveness and reliability of the proposed approach.

Machines doi: 10.3390/machines14050464

Authors: Zhenwei Wang Junjian Hou Dengfeng Zhao Zhijun Fu Fang Zhou Yudong Zhong Jinquan Ding

The fuel consumption of power-split hybrid electric vehicles (PSHEVs) can be effectively reduced via mode transition that includes the engine process. However, factors such as engine torque ripple, system parameter uncertainties, and variations in torsional vibration characteristics can easily induce drivetrain vibration. These factors not only degrade ride comfort but also lead to a fundamental control challenge. The inherent trade-off between rapid response and stability is difficult to reconcile. In addition, the lack of adaptive mechanisms further limits consistent performance under varying conditions. To tackle these problems, a scenario-adaptive composite robust control (SACRC) strategy is proposed. The strategy consists of a UIO (unknown input observer)-based torque observation module, an adaptive VSS-LMS approach, and an H∞ controller with self-tuning parameters. Firstly, a six-degree-of-freedom dynamic model of the PSHEV transmission system is established with excitation sources, considering the characteristics of dual elastic elements. Secondly, a UIO-based torque observer is designed using a simplified dual-elastic-element model. By using engine speed and output shaft speed, the observer can accurately identify the torque transmitted by the torsional damper and drive shaft. Then, an adaptive VSS-LMS and H∞ controller with self-tuning parameters is constructed to ensure a balanced performance between fast torsional vibration suppression and control stability. Finally, simulation and experimental results demonstrate that the proposed strategy provides favorable adaptability to complex scenarios, and unifies the performance goals of rapidity, stability, and robustness.

Machines doi: 10.3390/machines14050463

Authors: Rabab Hamed M. Aly Shimaa A. Hussien Marwa M. Ahmed Aziza I. Hussein

The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO2 emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R2 = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses.

Machines doi: 10.3390/machines14050462

Authors: Linjie Jin Zhengqing Liu Dawei Gu Baisong Pan Qiucheng Wang Mohammad Fard

Acoustic signal-based fault diagnosis offers a promising non-contact approach for rotating machinery. However, its practical application is usually affected by environmental noise. This paper presented a Denoising Attention Domain Adversarial Network (DDAN) for the robust fault diagnosis of wheel hub motors under severe noise interference. The proposed framework consists of the following two core modules: a DenseNet-based denoising module that adaptively suppresses background noise while retaining critical fault features, and a Stacked Autoencoder Domain Adversarial Network (SADAN) that integrates channel attention, spatial attention, and multi-head self-attention (MHSA) for refined feature extraction and classification. Such a hierarchical attention mechanism facilitates effective local noise suppression and global dependency capture. Validation on a hub motor fault dataset and publicly available online dataset demonstrates that compared to existing methods, DDAN achieves superior diagnostic accuracy across various noise levels and signal-to-noise ratios, improving SNR from -15.97 dB to 1.24 dB, achieving 82.71% accuracy under low SNR condition, and reaching 84.93% and 83.75% accuracy in cross-domain generalization tests. Furthermore, the comparison of the diagnostic accuracy of audio signals from different acoustic acquisition devices further verifies the practicality and potential of the system in low-cost industrial deployment.

Machines doi: 10.3390/machines14050461

Authors: Xinghui Wu Zewei Wang Xian Hong Wenjie Guo

Aiming at the problems of cumbersome parameter tuning and low computational efficiency in traditional methods for the bandgap optimization of periodic thin-walled stiffened coupled structures, this paper integrates the null-space method with the Kirchhoff thin-plate theory to establish an efficient model for bandgap analysis. The proposed method realizes matrix-based construction of coupled and periodic boundary conditions, decouples boundary constraints from displacement shape functions, avoids the limitations of virtual spring stiffness, and requires no remeshing during parameter variation. Comparisons with the finite element method verify its convergence and accuracy: the average deviation of bandgap widths in the 0–250 Hz range is 0.37 Hz, and the computational efficiency is about 2.5 times that of FEM(Finite Element Method). This paper also systematically analyzes the effects of four key parameters, including thin-wall thickness, stiffener thickness, stiffener height and stiffener spacing, on the number and width of bandgaps and proposes targeted optimization strategies for different engineering scenarios. The results provide a new method for vibration and noise reduction design of such structures and lay a foundation for future bandgap modeling and optimization of advanced lightweight periodic structures.

Machines doi: 10.3390/machines14050460

Authors: Hongyuan Zhu Jing Ni Nan Yang Boyang Gao Xiaoxiong Liu

A multi-task network for runway lines and runway markings based on deep learning was designed to address the issue of prior information dependence on runway width in unmanned aerial vehicle visual autonomous landing application scenarios. By detecting runway images captured at different positions during flight, the parameters of the runway start line, left and right boundary lines, and runway markings were obtained. On this basis, a runway width estimation model and visual positioning algorithm based on line features were designed. In standard runway scenarios, the recognition of runway signs provides valuable prior information about the runway width. For simplified runways or cases where signs are missing, we have devised a width estimation model based on the left/right boundary lines. Furthermore, considering the variation in pitch angle during the UAV’s landing process, we have analyzed and refined the width estimation model to ensure its applicability throughout the entire landing process. Additionally, we have developed a visual positioning algorithm that utilizes the runway width and runway line parameters to calculate the relative position between the UAV and the runway. Considering the limitations of a single visual positioning algorithm, we adopt a visual and inertial navigation fusion positioning algorithm to enhance the reliability of landing positioning. To validate our algorithms, we have constructed a visual simulation platform and flight test. These tests confirm the effectiveness and accuracy of our detection algorithm and width estimation model. Furthermore, by utilizing the estimated runway width and the detected runway line parameters, we have successfully calculated the relative position, further validating the effectiveness of our positioning algorithm.

Machines doi: 10.3390/machines14040459

Authors: Tiberiu-Daniel Pau Cristina Nine (Anton) Zoltan-Iosif Korka Dorian Nedelcu Attila Gerocs Elena Wisznovszky

Windage power losses (WPLs) can take a noticeable toll on the efficiency of high-speed gear transmissions, especially in helical gears, where complex 3D airflow patterns increase aerodynamic drag. In this work, we measured the WPL of a helical gear using a combination of analytical models, experiments, and CFD simulations. A custom test rig recorded windage losses at four speeds—2000, 3000, 4000, and 5000 rpm—producing values between 1.33 W and 21.67 W. We then compared these results with predictions from commonly used analytical methods (Dawson, Lord, ISO/TR 13593, ANSI/AGMA 6011-I03). These models showed discrepancies of about 25–35%, largely because they were not developed with helical gear geometries in mind. To complement this, CFD simulations carried out in SolidWorks Flow Simulation closely matched the experimental data, with an average deviation of just 4.99%. The combined results highlight the dominant mechanisms contributing to windage losses, assess the accuracy and limitations of each method, and identify the operating regimes where discrepancies are most pronounced. The findings offer a validated framework for predicting windage losses in industrial helical gears and support the development of more efficient gearbox designs.

Machines doi: 10.3390/machines14040458

Authors: Gai Liu Zihao Tang Yi Wang Shiyu Ruan Jinwei Zhang Chenyin Zhao Yizhou Hua

In order to solve the coupling problem of the six-pole hybrid magnetic bearing (SHMB), an active disturbance rejection control based on modified particle swarm optimization is proposed. Firstly, the active disturbance rejection control (ADRC) for the six-pole hybrid magnetic bearing is introduced, which is a second-order system. Secondly, the modified particle swarm optimization (MPSO) is used for the ADRC, which can decouple the SHMB. Then, simulations of SHMBs are performed under different disturbance signals, and floating simulations and anti-interference simulations of ADRC-MPSO and ADRC are compared. Finally, an experimental platform is established that verifies feasibility and reliability.

Machines doi: 10.3390/machines14040457

Authors: Jakub Wróbel Kamil Jendryka Maciej Milewski Artur Kierzkowski Michał Stosiak Olegas Prentkovskis Mykola Karpenko

This study presents experimental modal analysis of an ultra-lightweight composite structure representative of UAV application and to evaluate the suitability of different testing approaches for reliable identification of its dynamics characteristics. The investigated structure is a winglet made of carbon fiber reinforced polymer (CFRP) with a lightweight foam core. The experiment was based on impact hammer excitation combined with triaxial accelerometer measurements. Modal tests were performed under three different boundary conditions: free–free suspension using elastic cords, free–free approximation using compliant foam support, and fixed conditions reflecting the operational mounting of the winglet. The results confirm that boundary conditions constitute the dominant factor governing the dynamic response. Transition from free–free to fixed support shifted the dominant bending modal frequency from 331.5 Hz (single-sided response) and 329.9 Hz (double-sided response) 421.2 Hz in the fixed configuration, demonstrating a frequency increase of nearly 27%. Reciprocity and double-sided measurements revealed measurable frequency deviations (e.g., 116.3 Hz to 117.6 Hz) attributed to accelerometer mass loading and geometric misalignment. The 1 g triaxial accelerometer mass was shown to be non-negligible relative to the modal mass of the structure, producing observable shifts in higher-order modes.

Machines doi: 10.3390/machines14040456

Authors: Yuting Wang Liming Sun Bang An Ruiyun Yu

As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control.

Machines doi: 10.3390/machines14040455

Authors: Xiaoxiong Li Huafeng Ding

Planar multi-loop mechanisms often generate a large number of non-isomorphic candidate topological graphs during automatic synthesis, making it difficult to efficiently identify configurations that satisfy engineering-oriented functional requirements. To address this issue, a loop-constrained connectivity calculation method and a connectivity-based localization and screening procedure are proposed. The proposed connectivity calculation is directly formulated for general planar non-fractionated kinematic chains (NFKCs), including those with multiple joints. For planar fractionated kinematic chains (FKCs), however, the present method is not applied directly at the full-system level, but only to decomposed non-fractionated subchains after system-level decomposition. Starting from a structurally admissible set of candidate topological graphs, a connectivity matrix is established for automatic localization of the base and the end-effector (EE). Functional screening is then performed by combining the connectivity criterion with object-oriented rules on hydraulic driving-pair arrangement and driving-redundancy patterns. The method was validated using the 10-link, 3-DOF single-joint equivalent of the KC1 subchain of a mine scaler manipulator arm. Under the prescribed structural and functional constraints, 249 admissible configurations were obtained. The results indicate that the proposed method provides an effective basis for application-oriented topological screening and subsequent dimensional synthesis.

Machines doi: 10.3390/machines14040454

Authors: Yang Xu Zhixiong Li Chuan Sun Shucai Xu Haiming Sun Yicheng Cao Junru Yang

Complex weather degrades both perception reliability and tire–road adhesion, thereby reducing the safety margin and responsiveness of intelligent driving longitudinal control. This study proposes a reproducible evaluation method for adverse weather operational design domains based on parameter perturbation testing and comprehensive assessment. Snow, fog, and rain are graded using standard quantitative thresholds and are coupled with road slipperiness to construct a weather–road state set. A mechanism-oriented indicator system, a combined subjective–objective weighting strategy, and a multi-level fuzzy comprehensive evaluation model are then used to generate quantitative capability scores. The method is validated on a co-simulation framework integrating vehicle–sensor simulation, a driving simulator, and a digital-twin testing environment using representative autonomous emergency braking scenarios. Results show that increasing weather severity, decreasing road adhesion, and higher initial speed reduce the post-braking safety margin and prolong collision-response time. The proposed method differentiates performance across weather–road states and provides quantitative support for test-coverage planning and capability boundary calibration in adverse weather operational design domains.

Machines doi: 10.3390/machines14040453

Authors: Jianming Du Haihang Gao

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems.

Machines doi: 10.3390/machines14040452

Authors: Yunhao Zhang Huafeng Ding

This paper investigates the cooperative formation control problem in unmanned surface vehicles (USVs) with prescribed performance constraints under complex marine conditions including external disturbances, model uncertainties, actuator faults, and input saturation. A novel fault-tolerant control (FTC) algorithm is developed by integrating cooperative learning neural networks (NNs), distributed disturbance observers, and the backstepping technique. Specifically, the learning NNs adaptively approximate system uncertainties, and the learned weight information is shared among vehicles to enhance cooperative cognition. Additionally, an auxiliary dynamic system and an actuator configuration matrix are designed to compensate for input saturation and propeller failures. Theoretical analysis based on the Lyapunov method proves that all signals in the closed-loop system are bounded, and the formation tracking errors strictly remain within the predefined transient and steady-state performance bounds. Finally, simulation experiments involving a group of four USVs validate the proposed algorithm. The results demonstrate that the USVs can rapidly converge to and maintain the desired quadrilateral formation shape despite time-varying disturbances and actuator efficiency loss. Furthermore, comparative simulation results indicate that the proposed cooperative learning FTC scheme significantly reduces velocity tracking error oscillations compared to traditional non-learning methods, explicitly verifying its superior robustness and fault-tolerant capabilities.

Machines doi: 10.3390/machines14040451

Authors: Ye Yuan Yupeng Shi Dingxuan Zhao Wei Wang Qian Cheng

Accurately determining digging resistance during bucket–soil interaction is crucial for optimizing excavator working devices and power systems. To address measurement difficulties, a numerical simulation model based on the arbitrary Lagrangian–Eulerian (ALE) method was established and verified through excavation tests. Through orthogonal experiments, the influence of excavation parameters was studied, and the optimal compound digging trajectory was determined. The results show that increasing the excavation angle from 36° to 48° decreases the X-direction resistance and moment by 39.48% and 38.85%, respectively, though specific energy consumption (SE) increases. Additionally, optimizing arm movement speed reduces the X-direction resistance and moment. While ensuring the bucket load factor is suitable, reducing arm speed and a horizontal soil push during compound excavation effectively decreases SE. Finally, the optimal balance of digging resistance and SE can be achieved with a 300 mm bucket hydraulic cylinder displacement, a 1.5 s interval for initial arm and bucket movements, and an arm-to-bucket speed ratio of 5.5 for hydraulic cylinders.

Machines doi: 10.3390/machines14040450

Authors: Georgios Gkoumas Panagis Foteinopoulos Ivelin Andreev Marian Graurov Panagiotis Stavropoulos

The increasing demand for energy, rising electricity costs, and the growing need to reduce carbon emissions have driven industries toward the adoption of Renewable Energy Sources (RES) and Energy Storage Systems (ESS). However, selecting the most suitable ESS for industrial peak-shaving applications remains a complex decision involving technical, economic, and operational considerations. This paper proposes a practical and structured methodology for ESS selection that integrates conventional performance criteria with Industry 5.0 (I5.0) requirements, emphasizing sustainability, resilience, and human-centric industrial operation. Unlike existing multi-criteria decision-making approaches, the proposed framework reduces reliance on expert-based weighting, improving transparency and reproducibility. The methodology is implemented in two stages: initial KPI-based shortlisting of technologies, followed by detailed comparative performance analysis. A case study conducted in a European tire manufacturing plant compares lithium-ion batteries and flywheel energy storage systems under different peak-shaving strategies. Lithium-ion batteries demonstrated superior performance, covering approximately 80% of demand peaks compared with the 73% achieved by the flywheel system, confirming the effectiveness of the proposed methodology for practical industrial ESS selection.

Machines doi: 10.3390/machines14040449

Authors: Eva M. Rubio Marian Sáenz-Nuño Marta M. Marín David Gómez

Recent advances in machine design, automation, and industrial digitalization have transformed Coordinate Measuring Machines (CMMs) from standalone inspection devices into fully integrated elements of automated manufacturing systems. In the automotive sector, CMMs increasingly operate in workshop, near-line, and in-line environments, interacting with production equipment and contributing directly to process control and zero-defect manufacturing strategies. This paper presents a structured methodology for the industrial deployment of automated CMMs in automotive mechanical manufacturing. The proposed approach is illustrated through an industrial use case involving the dimensional inspection of mechanically machined components under real production conditions. The methodology addresses machine design selection, sensor configuration, environmental constraints, and multi-axis architectures, as well as validation and acceptance procedures based on the ISO 10360 series. Particular attention is given to the integration of CMMs within automated manufacturing systems, including robustness against thermal variations, vibrations, and contamination, and the use of metrological data for feedback to machining processes. Rather than introducing new metrological principles, the proposed approach focuses on the structured integration of established engineering practices into a coherent lifecycle-based deployment framework. Based on industrial experience, the proposed methodology is illustrated through an industrial case study to support the reliable of automated dimensional inspection, reduce measurement-related risks, and support the integration of CMMs as active components of modern automated manufacturing systems.

Machines doi: 10.3390/machines14040448

Authors: Cristina Popa Sorin Cănănău George Ghiocel Ojoc Cătălin Pîrvu Mario Constandache Valentin Azamfirei Lorena Deleanu

Machine components are subject to a wide range of damage and failure processes, and their correct identification is essential for reliable operation, effective maintenance, and accurate diagnosis. However, a persistent gap exists between morphology-based classification systems, used in international standards, and the mechanism-based interpretations developed in tribology and mechanics. This review analyzes the evolution and current practice of damage classification for machine components, with emphasis on rolling bearings as a representative case. The study is based on a structured analysis of international standards (e.g., ISO 15243), complemented by tribological literature and manufacturers’ documentation. The review focuses on how several damage modes such as spalling, pitting, and fretting are defined, interpreted, and applied in practice. The results highlight systematic ambiguities arising from the separation between visual descriptors and underlying failure mechanisms, particularly in the case of fatigue-related surface damage. Through selected case studies, the review demonstrates how reliance on morphology alone may obscure causal interpretation and lead to inconsistent diagnosis. The study further discusses emerging trends, including digital damage atlases and artificial-intelligence-based diagnostic tools, emphasizing how inconsistencies in standardized terminology may affect their reliability. It is concluded that morphology-based standards should be regarded as complementary diagnostic tools rather than substitutes for mechanical analysis. A closer alignment between standardized terminology and mechanistic understanding is necessary to improve failure diagnosis, support engineering education, and enhance the reliability of machine components.

Machines doi: 10.3390/machines14040447

Authors: Xiang Li Pengxiang Wang Yuchun Xu Jihong Yan

An automotive welding unit is a modular production cell within a welding workshop that integrates industrial manipulators, welding equipment, fixtures, and control systems to perform specific welding and assembly tasks. A large number of industrial manipulators are utilized in the automotive welding unit. The capability to quickly plan a short and collision-free path in the workspace of the manipulator is of great importance for improving the manipulator’s intelligence level and production efficiency. The RRT* algorithm, based on random sampling, has been widely applied in path planning for high-dimensional manipulators due to its probabilistic completeness and powerful exploration capabilities. However, the RRT* algorithm performs poorly in spaces containing narrow passages. Research on the practical application of path planning for 6-DOF manipulators is still insufficient, particularly in planning posture. To solve these two problems, an improved RRT* algorithm is proposed in this paper. New sampling and node connection strategies are designed to improve the expansion and convergence speed of the random tree in spaces containing narrow passages. A distance-constrained posture quaternion interpolation method is presented to generate smooth and continuous paths for manipulators of the automotive welding unit. Simulations and experiments are carried out to validate the proposed method, which confirms that the method can plan collision-free paths for manipulators more quickly compared to other methods.