Machines

29 pages, 3991 KB

Open AccessArticle

An Agile Innovation Design Method via Integrating LT Dimension and TRIZ

by Kang Wang, Yaqiang Zhu, Qingjin Peng and Runhua Tan

Machines 2026, 14(6), 657; https://doi.org/10.3390/machines14060657 (registering DOI) - 5 Jun 2026

Agile innovation is becoming increasingly important for complex mechatronic products. Existing studies often remain at the project management level and offer limited operational guidance for conceptual structural design. This paper proposes an agile innovation design method that integrates the Length–Time (LT) dimension and [...] Read more.

Agile innovation is becoming increasingly important for complex mechatronic products. Existing studies often remain at the project management level and offer limited operational guidance for conceptual structural design. This paper proposes an agile innovation design method that integrates the Length–Time (LT) dimension and Theory of Inventive Problem Solving (TRIZ) to translate user feedback into engineering-oriented conceptual solutions. First, user pain points are organized into a fishbone-based functional model, and core problems are mapped to LT dimensions using a natural-language processing rule set. Second, a neural network trained on cases of technological evolution predicts the corresponding TRIZ evolution law. Third, structurally similar engineering cases are retrieved based on LT-dimensional similarity and transformed into conceptual schemes by structural mapping. Finally, the technique for order preference by similarity to an ideal solution is used to rank alternative schemes with explicit normalization, distance calculation, and sensitivity checking. The method is demonstrated through the conceptual redesign of a vertical-axis wind turbine. Full article

(This article belongs to the Section Machine Design and Theory)

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15 pages, 3407 KB

Open AccessArticle

A Diameter-Varying Spherical Robot and the Locomotion Analysis with Physical Simulation

by Sota Amano, Renke Liu and Hideyuki Sawada

Machines 2026, 14(6), 656; https://doi.org/10.3390/machines14060656 (registering DOI) - 5 Jun 2026

Abstract

This paper introduces a novel diameter-varying spherical robot that locomotes by local shell deformation and rolling. The robot is based on a dodecahedron-inspired shell structure equipped with 12 movable outer modules driven by a central screw-driven lifting mechanism. Unlike conventional spherical robots that [...] Read more.

This paper introduces a novel diameter-varying spherical robot that locomotes by local shell deformation and rolling. The robot is based on a dodecahedron-inspired shell structure equipped with 12 movable outer modules driven by a central screw-driven lifting mechanism. Unlike conventional spherical robots that mainly use the internal mass shift or internal rotating units inside a rigid shell, the proposed robot changes its locally deforming geometry by extending selected outer modules. To investigate the resulting rolling behavior, a physical simulation environment was constructed in Unreal Engine coupled with an external Python controller through OSC-based communication. The physical simulation preserves the essential geometry of the robotic body including control circuits situated in the core part and provides a repeatable platform for observing contact-driven rolling locomotion under time-managed operation. As the initial operational observation of the diameter-varying spherical robot, hardware observations were conducted to examine whether the same deformation-induced rolling principle could be physically realized in the fabricated robot. The paper presents the design of the spherical robot, the locomotion principle, physical simulation environment and the rolling simulation, and the experimental verification of the fabricated robot. Full article

(This article belongs to the Special Issue Intelligent Applications in Mechanical Engineering)

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22 pages, 1708 KB

Open AccessArticle

Few-Shot Fault Diagnosis of Rotating Machinery Using Complex Convolution and Disentangled Representation Learning

by Qiuyang Zhou, Xiaoyu Xian, Zhengyu Chen, Lei Yan, Yuming Fan and Kexin Yin

Machines 2026, 14(6), 655; https://doi.org/10.3390/machines14060655 (registering DOI) - 4 Jun 2026

Abstract

Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting and may fail to extract stable fault-sensitive features from [...] Read more.

Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting and may fail to extract stable fault-sensitive features from vibration signals. Moreover, the weak fault-related components are usually coupled with operating-condition variations, background vibration, and environmental noise, which further degrades the discriminability and generalization ability of diagnostic models. To address these problems, this paper proposes a complex-valued disentangled representation learning network for few-shot fault diagnosis of rotating machinery. First, a direction-pair complex augmentation strategy is developed for triaxial vibration measurements. Two directional vibration components are selected and organized as the real and imaginary branches of a complex-valued input, which increases sample diversity under few-shot conditions. Then, a lightweight complex-valued convolution block is designed to model the coupled dynamic characteristics between different vibration directions and extract fault-sensitive representations. Furthermore, a dual-branch disentangled representation structure is developed to decompose the learned features into fault-sensitive representations and condition-related interference representations. To enhance the separability of fault embeddings under limited samples, a cosine-based disentangled representation loss is introduced, which improves intra-class compactness and inter-class discrimination while suppressing irrelevant interference information. Finally, a few-shot diagnosis strategy is constructed to identify fault categories with only a small number of labeled samples. Experimental results demonstrate that the proposed method consistently outperforms representative methods in terms of diagnostic accuracy, feature separability, and robustness, especially under extremely limited labeled samples. Full article

(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)

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34 pages, 2416 KB

Open AccessArticle

Deep Reinforcement Learning for Variable Tension Control of Unmanned Underwater Vehicle Arresting Gear Under Nonlinear Effects

by Xikun Wang, Weijia Li, Junlei Huang and Fayou Liu

Machines 2026, 14(6), 654; https://doi.org/10.3390/machines14060654 - 4 Jun 2026

Abstract

Large Unmanned Underwater Vehicles (UUVs) are playing an increasingly critical role in complex marine missions due to their enhanced payload and endurance capabilities. However, the safe recovery of these platforms remains a significant challenge, complicated by their high inertia, strong hydrodynamic interactions, and [...] Read more.

Large Unmanned Underwater Vehicles (UUVs) are playing an increasingly critical role in complex marine missions due to their enhanced payload and endurance capabilities. However, the safe recovery of these platforms remains a significant challenge, complicated by their high inertia, strong hydrodynamic interactions, and unpredictable environmental disturbances. In particular, the nonlinear coupling effects between the mechanical structure and the hydrodynamic environment exert a considerable influence on the system, accounting for nearly 50% of the tension on the arresting cable. To address these challenges, this paper proposes a variable tension control strategy for a UUV underwater arresting recovery system, utilizing a Well-Shaped Reward Entropy-regularized Proximal Policy Optimization (WSR-E-PPO) algorithm. In this framework, the real-time velocity and displacement of the UUV are utilized to represent the spatiotemporal characteristics of the recovery state, and a hybrid reward function integrating sparse and continuous rewards based on Potential-Based Reward Shaping (PBRS) is designed. Simulation results demonstrate that the proposed method enables the UUV to return to the docking point without oscillation, while effectively limiting the total recovery time to approximately 80 s—a 37.5% reduction compared with existing methods. Furthermore, the strategy ensures smoother tension regulation throughout the process. These findings provide a solid technical foundation and assurance for the stable and safe underwater recovery of large UUVs. Full article

(This article belongs to the Section Automation and Control Systems)

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14 pages, 6007 KB

Open AccessArticle

Uncertainty and Sensitivity Analyses of an Annular Thermoelectric Refrigerator Based on Latin Hypercube Sampling

by Jinhao Ma, Meilin Song, Xue Li and Feng Zhang

Machines 2026, 14(6), 653; https://doi.org/10.3390/machines14060653 - 4 Jun 2026

Abstract

Thermoelectric refrigeration systems employing the Peltier effect are widely applied in electronic cooling and environmental control. The performance of such systems is influenced by inevitable uncertainties in key parameters that can lead to noticeable variations in system responses. However, previous sensitivity analyses have [...] Read more.

Thermoelectric refrigeration systems employing the Peltier effect are widely applied in electronic cooling and environmental control. The performance of such systems is influenced by inevitable uncertainties in key parameters that can lead to noticeable variations in system responses. However, previous sensitivity analyses have been limited to flat-plate thermoelectric systems; annular thermoelectric refrigeration systems have not been analyzed. Accordingly, this study conducted a dispersion analysis of an annular thermoelectric refrigeration system by treating the input parameters as random variables that follow normal distributions and generating samples using Latin hypercube sampling. The changes in three response indicators—the cold-end temperature, refrigeration efficiency, and exergy efficiency—with small shifts in the mean and standard deviation of each input parameter were subsequently calculated to determine their influence on system performance. The results indicated that a slight decrease in the hot-side temperature significantly enhanced overall system performance, with an average influence weight of 0.45, and a moderate increase in the cold-side heat absorption improved both the refrigeration and exergy efficiencies, with an average influence weight of 0.29; by contrast, changes in the annular geometric parameter had relatively weak effects on system performance and the dispersion of system responses, exhibiting an average influence weight of only 0.02. Therefore, this study demonstrated an effective approach for evaluating the uncertainty inherent in annular thermoelectric refrigeration systems and provides a reference for optimizing their designs to ensure reliability. Full article

(This article belongs to the Special Issue Advances in Automotive Mechatronics)

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26 pages, 1267 KB

Open AccessArticle

Laser Beam Welding State Classification: A Deep Learning Framework for Acoustic Signal Intelligence

by Erkan Caner Ozkat

Machines 2026, 14(6), 652; https://doi.org/10.3390/machines14060652 - 4 Jun 2026

Abstract

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework [...] Read more.

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds,

f_{s}

= 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding. Full article

(This article belongs to the Special Issue Laser-Driven Manufacturing: Cutting, Welding, and Surface Modification Technologies)

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17 pages, 3709 KB

Open AccessArticle

Influence of Planet Phasing on Quasi-Static Transmission Error in Planetary Spur Gears with Profile Modifications

by José I. Pedrero, Miguel Pleguezuelos, Andrés F. Hidalgo and Miryam B. Sánchez

Machines 2026, 14(6), 651; https://doi.org/10.3390/machines14060651 - 4 Jun 2026

Abstract

In a planetary gear system, the planet phasing depends on the number of teeth in the sun and the ring and the number of planets. When the tooth numbers are both multiples of the number of planets, all planets mesh at the same [...] Read more.

In a planetary gear system, the planet phasing depends on the number of teeth in the sun and the ring and the number of planets. When the tooth numbers are both multiples of the number of planets, all planets mesh at the same relative position—which is called synchronous configuration—and the input torque is shared evenly among them. Otherwise, the configuration is asynchronous, or sequentially phased, and the torque sharing is uneven. This directly influences the instantaneous load sharing between the external planet–sun and internal planet–ring meshes, consequently altering both load-induced tooth deflections and the resulting transmission error. The profile relief, frequently used to avoid the mesh-in impact, influences the teeth contact along the interval of relief, which also affects the load distribution, mesh stiffness, and transmission error. Since the transmission error is a source of dynamic load, noise, and vibrations, its peak-to-peak amplitude should be controlled, and the geometry of the profile modification provides an efficient tool. In this paper, the transmission error of spur planetary gears is studied with an analytical model previously developed, based on the minimum elastic potential energy. The study also assesses the influence of the depth and length of the tip relief and compares the behavior of synchronous and asynchronous configurations. As a result of this analysis, it has been found that the variation in the amplitude of transmission error is significantly lower in sequentially phased configurations and reaches the minimum variation for the adjusted depth of relief and medium length of relief. Furthermore, an odd number of teeth on the planets results in a higher mesh stiffness than an even number, which induces a slightly lower peak-to-peak transmission error. Full article

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4 pages, 138 KB

Open AccessEditorial

Editorial for the Special Issue “Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision”

by Yanhong Peng and Fernando Gomez-Bravo

Machines 2026, 14(6), 650; https://doi.org/10.3390/machines14060650 - 4 Jun 2026

Abstract

Artificial intelligence is increasingly reshaping the foundations of robotics by enabling machines to perceive complex environments, learn from interaction, make adaptive decisions, and collaborate with humans in a safe and effective manner [...] Full article

(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)

23 pages, 22737 KB

Open AccessArticle

Applications of Bone Staples and Feasibility of Replacing Nitinol with Nitinol-Free β-Ti Alloys in Bone Staples

by Zhuming Bi, Song Cai and Jeremy Schaffer

Machines 2026, 14(6), 649; https://doi.org/10.3390/machines14060649 - 3 Jun 2026

Abstract

Bone staples that are in fixation of fractures or arthrodesis are mostly fabricated using Nitinol. While the attractiveness of this type of staple is widely recognized, concerns have been raised about the suitability of these staples in patients who are allergic to nickel. [...] Read more.

Bone staples that are in fixation of fractures or arthrodesis are mostly fabricated using Nitinol. While the attractiveness of this type of staple is widely recognized, concerns have been raised about the suitability of these staples in patients who are allergic to nickel. In this paper, current studies on bone staples are discussed and have found that existing bone staples are mostly Nitinol-based and are inapplicable to some patients who are allergic to Nitinol. The concept of digital triad (DT-II) is introduced to model, verify and validate bone staples with the consideration of installing parameters in an inserting process. A design study is defined to investigate the impact of two main inserting parameters on clamping forces of a staple. The study leads to the conclusions that with an appropriate adjustment on inserting parameters, new Ni-free β-Ti Alloy can be used to produce bone staples with clamping forces equivalent to those that are made from Nitinol-based materials. These findings may guide orthopedic surgeons in the selection of bone staples for use in a given fracture fixation or arthrodesis case. Full article

(This article belongs to the Special Issue Advanced Digital Twin in Smart Manufacturing)

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23 pages, 2075 KB

Open AccessArticle

Research on Optimal Morphing Strategies for Multi-Performance of UAV

by Long Tan, Chao Yang and Yu Wang

Machines 2026, 14(6), 648; https://doi.org/10.3390/machines14060648 - 3 Jun 2026

Abstract

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome [...] Read more.

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs. Full article

(This article belongs to the Special Issue Smart Structures and Applications in Aerospace Engineering)

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27 pages, 12776 KB

Open AccessArticle

Robust Fault-Tolerant Iterative Learning Control for Spatially Interconnected Systems in the Frequency Domain

by Lei Wang, Menghan Wei, Shunjie Zhu, Xiaoxiao Wang and Xuejian Ge

Machines 2026, 14(6), 647; https://doi.org/10.3390/machines14060647 - 3 Jun 2026

Abstract

This study developed a frequency-domain fault-tolerant iterative learning control (ILC) strategy for spatially interconnected systems subject to external disturbances and actuator faults. An equivalent one-dimensional system model was established by performing model transformation and reformulation on the original spatially interconnected system. On this [...] Read more.

This study developed a frequency-domain fault-tolerant iterative learning control (ILC) strategy for spatially interconnected systems subject to external disturbances and actuator faults. An equivalent one-dimensional system model was established by performing model transformation and reformulation on the original spatially interconnected system. On this basis, the iterative learning control law was designed using two-dimensional system theory, and the closed-loop system was further characterized as an iterative process model. By applying the generalized Kalman–Yakubovich–Popov (KYP) lemma, it was demonstrated that within a finite frequency range, the resulting discrete repetitive process can guarantee both batch-wise stability and monotonic asymptotic convergence of the tracking error. Experimental results obtained from a precision collaborative machining platform verify the effectiveness of the proposed control method. Full article

(This article belongs to the Section Automation and Control Systems)

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25 pages, 7126 KB

Open AccessArticle

FEM-Based Stress and Fatigue Assessment of UIC Screw Couplings Under Traction–Emergency Braking Loads

by Edoardo Risaliti, Francesco Del Pero, Andrea Antonacci and Gabriele Arcidiacono

Machines 2026, 14(6), 646; https://doi.org/10.3390/machines14060646 - 3 Jun 2026

Abstract

Railway screw couplings are safety-critical, yet service failures show fatigue cracking at geometric discontinuities. This work assesses the response of two UIC screw-coupling components—the shackle and trunnion—under longitudinal forces from Traction–Emergency Braking (TEB) manoeuvres. A linear-elastic 3D finite element model was built for [...] Read more.

Railway screw couplings are safety-critical, yet service failures show fatigue cracking at geometric discontinuities. This work assesses the response of two UIC screw-coupling components—the shackle and trunnion—under longitudinal forces from Traction–Emergency Braking (TEB) manoeuvres. A linear-elastic 3D finite element model was built for 42CrMo4/AISI 4140 steel, idealising the threaded load transfer with an RBE2 condensation and the hook–shackle interface with a tied contact to provide a repeatable baseline. Longitudinal force histories were generated in TrainDy for a freight consist and mapped to Regions of Interest; fatigue was evaluated in Altair HyperLife using rainflow counting, Goodman mean-stress correction, and Palmgren–Miner accumulation on a uniaxial S-N curve. For the 636 kN envelope case, the model predicts an axial displacement of 0.985 mm and von Mises stresses in several relevant regions near the nominal yield strength. Fatigue results rank the trunnion pin fillet as the governing hotspot: representative TEB sequences yield damage indices greater than 1 (often of order 20), while a lower-amplitude braking block shows negligible damage. Overall, the analysed spectra leave little endurance margin for the current geometry and support redesign of critical radii and more realistic contact/boundary modelling. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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19 pages, 35640 KB

Open AccessArticle

An MR-HRI Framework for Mobile Devices to Communicate Force Intent and Receive Visual Force Feedback

by Christian Lourido, Kishan Reddy Raghunath and Vikram Kapila

Machines 2026, 14(6), 645; https://doi.org/10.3390/machines14060645 - 3 Jun 2026

Abstract

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality [...] Read more.

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality (MR) technology offers a compelling approach for designing innovative models of human–robot interaction (HRI). For robot manipulators, mobile MR frameworks that allow a user to communicate a goal position for the robot’s end effector have been widely studied. However, HRI applications that may require other relevant information for the manipulator to complete more complex tasks remain unexplored. Thus, we propose an MR-enhanced HRI framework, deployed on a touchscreen tablet, that utilizes a virtual arrow object to communicate force intent (i.e., location, direction, and magnitude) to the manipulator and provide visual force feedback to the user. To evaluate the system performance and user experience, we conducted a user study with 25 participants who used a manipulator robot to complete four insertion subtasks, reporting a task success score of 96%, a usability overall mean score of 4.35 out of 5, and a low task load index of 21.49 out of 100. The results show that the MR-HRI framework is intuitive to operate, allowing users to successfully perform assigned tasks by effectively communicating their intentions through the virtual arrow. Full article

(This article belongs to the Special Issue The New Digital Era in Industrial Robotics, Mechatronics and Factory Automation)

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13 pages, 8645 KB

Open AccessArticle

Stochastic Mask Causal Graph Network for Industrial System Fault Diagnosis

by Jiajia Zhang and Weijun Zhang

Machines 2026, 14(6), 644; https://doi.org/10.3390/machines14060644 - 2 Jun 2026

Abstract

Despite their demonstrated effectiveness in modeling sensor interaction networks for industrial fault diagnosis, graph neural networks (GNNs) still encounter two key limitations: black-box operation that lacks transparency in fault identification and propagation analysis, and unreliable attention mechanisms whose weights fail to faithfully reflect [...] Read more.

Despite their demonstrated effectiveness in modeling sensor interaction networks for industrial fault diagnosis, graph neural networks (GNNs) still encounter two key limitations: black-box operation that lacks transparency in fault identification and propagation analysis, and unreliable attention mechanisms whose weights fail to faithfully reflect the genuine relevance of sensors or their interactions. To tackle these challenges, we put forward the Stochastic Mask Causal Graph Network, a novel framework that integrates a learnable stochastic masking mechanism guided by the information bottleneck principle. Unlike conventional attention-based or post-hoc approaches, our method automatically suppresses label-irrelevant graph components while preserving causally relevant structures, thereby providing faithful inherent interpretability without biased assumptions and effectively removing spurious correlations to enhance generalization. Comprehensive experiments on realistic complex industrial system datasets demonstrate that the proposed method achieves superior diagnostic accuracy and enhanced interpretability compared with existing advanced approaches. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)

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21 pages, 28578 KB

Open AccessArticle

Development and Validation of a Scanning Device Based on Consumer-Grade TrueDepth Sensors

by Julián Álvarez, Alejandro Fernández, Pablo Zapico, Natalia Beltrán, Pedro Fernández and David Blanco

Machines 2026, 14(6), 643; https://doi.org/10.3390/machines14060643 - 2 Jun 2026

Abstract

This work presents the development and validation of an automated 3D scanning device based on two opposed consumer-grade Apple TrueDepth sensors integrated into a controlled rotational architecture, designed for the digitization of complex freeform surfaces such as the external cranial geometry. The system [...] Read more.

This work presents the development and validation of an automated 3D scanning device based on two opposed consumer-grade Apple TrueDepth sensors integrated into a controlled rotational architecture, designed for the digitization of complex freeform surfaces such as the external cranial geometry. The system design was guided by a prior metrological characterisation of the sensor’s distance-dependent behaviour and complemented by an additional study of the influence of surface orientation, from which a suitable operating window for complete head acquisition was derived. On this basis, a mechatronic system was implemented comprising a mechanical structure, electronic hardware, a control architecture, and a calibration procedure that registers the local point clouds from both sensors into a common global coordinate system. Geometric validation was performed using symmetric and asymmetric cranial phantoms digitized with both the proposed device and a professional reference scanner. Surface comparison revealed localized discrepancies concentrated in fine anatomical details, while the cranial vault showed good overall agreement, with RMS deviations of 0.314 mm and 0.286 mm for the symmetric and asymmetric phantoms, respectively. Morphometric consistency was assessed through the cranial vault asymmetry index (CVAI), for which both systems produced the same general trend with a maximum difference of 0.2%. These results demonstrate the feasibility of the proposed system as a geometrically consistent and morphometrically reliable instrument for head surface digitization under controlled laboratory conditions. Full article

(This article belongs to the Special Issue Dimensional Measurement, Analysis, and Control for Precision Manufacturing)

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26 pages, 758 KB

Open AccessArticle

Adaptive Optimal Speed Tracking Control of a PMSM Integrated with Linear Quadratic Integral Control for the Peak DC-Link Voltage Regulation of Quasi-Z-Source Inverters in All-Electric Aircraft

by Cong-Thanh Pham, Thanh-Dat Mai, Duc Thien Huynh and Hien Bui Van

Machines 2026, 14(6), 642; https://doi.org/10.3390/machines14060642 - 2 Jun 2026

Abstract

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor [...] Read more.

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

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27 pages, 3787 KB

Open AccessArticle

Direct Surface-Based Meshing and Measurement-Driven Cutter Edge Reconstruction for Cylindrical Gear Skiving

by Wei-Jen Chen and Zhang-Hua Fong

Machines 2026, 14(6), 641; https://doi.org/10.3390/machines14060641 - 2 Jun 2026

Abstract

Most existing analytical models for gear skiving define the cutting edge indirectly through hypothetical generating gears. This abstraction introduces an inherent geometric inconsistency between the theoretical cutting edge and the true conical rake surface of the cutter, limiting prediction accuracy and hindering measurement-driven [...] Read more.

Most existing analytical models for gear skiving define the cutting edge indirectly through hypothetical generating gears. This abstraction introduces an inherent geometric inconsistency between the theoretical cutting edge and the true conical rake surface of the cutter, limiting prediction accuracy and hindering measurement-driven compensation. To address this limitation, this study proposes a unified analytical and measurement-driven framework for cylindrical gear skiving that eliminates the generating-gear assumption entirely. A direct surface-based meshing formulation is developed by enforcing positional coincidence and tangential compatibility directly on the cutter’s conical rake surface, ensuring strict geometric consistency with the physical cutting mechanism. To incorporate real cutter deviations, a tension-controlled spline reconstruction method is introduced to recover smooth and curvature-stable cutting edge curves from noisy three-dimensional measurement data, overcoming the oscillation and instability commonly associated with high-order polynomial fitting. By integrating direct surface-based meshing, spline-regularized reconstruction, and CNC-oriented kinematics within a single formulation, this work establishes a complete digital chain for precision skiving cutter modeling, simulation, and compensation, providing a practical foundation for advanced tool design and manufacturing optimization. Full article

(This article belongs to the Section Machine Design and Theory)

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27 pages, 22042 KB

Open AccessArticle

Study of Dynamic Analysis and Structural Parameters Optimization of the Air-Jet Loom Frame System

by Jiacheng Zhou, Zhuo Chen, Shengli Lv, Libin Zhang, Feng Hu, Min Shen, Fei Fan, Lianqing Yu, Mingzhang Chen, Chengcheng Wang and Xiaoshuang Xiong

Machines 2026, 14(6), 640; https://doi.org/10.3390/machines14060640 - 1 Jun 2026

Abstract

Air-jet looms are widely used for weaving lightweight fabrics due to their outstanding high performance. To enhance the overall structural strength of air-jet looms and reduce operational vibration, dynamic analysis and structural parameter optimization of the loom frame system were carried in this [...] Read more.

Air-jet looms are widely used for weaving lightweight fabrics due to their outstanding high performance. To enhance the overall structural strength of air-jet looms and reduce operational vibration, dynamic analysis and structural parameter optimization of the loom frame system were carried in this study. First, after the structural designing and finite element modeling, the modal tests of the loom frame system were conducted. The modal results showed the high consistency between the simulation and experiment, confirming the accuracy of the dynamic model. Then, the dynamic characteristics and maximum stress data of the frame were obtained and analyzed through vibration tests and simulation calculations. The results indicated that the maximum deformation occurred at the middle of the beam while the maximum stress occurred at the connection between the lower beam and the wall panel. Moreover, the loom frame parameter optimization model was constructed based on the BA-HHO-SVR (Balanced Adaptive—Harris Hawks Optimization—Support Vector Regression) algorithm, demonstrating excellent learning and predictive capabilities. Eventually, the optimal combination of the frame structural parameters was obtained through above optimization algorithm. Furthermore, the effectiveness and reliability of the optimal combination were verified by finite element calculations. The vibration analysis method and optimization strategy proposed in this study provide valuable guidance for subsequent structural design and optimization of the high-end textile equipment. Full article

(This article belongs to the Section Machine Design and Theory)

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27 pages, 2037 KB

Open AccessArticle

Development of a Robust-Adaptive Fault-Tolerant Control Algorithm Enhanced by Data-Driven Actuator Fault Estimation for a Multirotor UAV

by Karim Ahmadi Dastgerdi and Seyed-Yaser Nabavi-Chashmi

Machines 2026, 14(6), 639; https://doi.org/10.3390/machines14060639 - 1 Jun 2026

Abstract

Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) [...] Read more.

Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) is employed to estimate actuator fault using IMU measurements. The ANN is trained using data generated from a closed-loop system controlled by a robust-adaptive control, rather than an open-loop configuration, improving its ability to capture realistic fault dynamics. To mitigate limitations in training data coverage, an adaptive mechanism is incorporated to enhance robustness under varying operating conditions. In addition of inherent fault-tolerant control characteristics of the robust-adaptive control, the estimated fault signals are used for motor speed compensation to enhance the robustness of the algorithm. The inner-loop controller is designed based on a robust-adaptive algorithm, ensuring system stability and robustness against model uncertainties and fault estimation errors, even the actuator faults change both system dynamics and actuation. The outer-loop Proportional–Integral–Derivative (PID) controller is employed to achieve accurate trajectory tracking. For validation and benchmarking, a standalone robust-adaptive controller and a model-based recursive least squares (RLS) estimator are also implemented. Simulation results demonstrate that the proposed ANN-based approach provides accurate fault estimation and effective compensation, resulting in improved tracking performance under actuator fault conditions. Furthermore, the proposed framework contributes to the development of a fault-tolerant UAV systems by integrating robust-adaptive control, ANN-based fault estimation, and actuator compensation into a unified architecture, thereby enhancing reliability, robustness, and tracking performance in the presence of actuator faults and modeling uncertainties. Full article

(This article belongs to the Special Issue Innovative Applications and Challenges of Intelligent Automation and Control in Smart Machines)

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