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Volume 14
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Machines, Volume 14, Issue 3 (March 2026) – 105 articles

Cover Story (view full-size image): This paper presents the design, analysis, and prototyping of a Flux Switching Permanent Magnet (FSPM) motor for Formula SAE electric vehicles. A preliminary analytical model based on airgap permeance and magnetomotive force is employed to define the main machine geometry, which is then validated through finite element analysis. Further design refinements include the adoption of laminated magnets and a lightweight rotor geometry to reduce losses and mass. Manufacturing-oriented considerations are integrated into the final design, and the main steps of the manufacturing process are also outlined. View this paper
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28 pages, 4224 KB  
Article
Path Tracking Control for Differential Steering Autonomous Vehicles with Active Body Inward Tilt
by Rizwan Ali, Chenyu Huang, Tong Wu and Jie Tian
Machines 2026, 14(3), 357; https://doi.org/10.3390/machines14030357 - 23 Mar 2026
Viewed by 855
Abstract
Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles [...] Read more.
Considering the problems that the inner wheel load decreases due to centrifugal force during the steering of differential steering autonomous vehicles, which may result in differential steering failure or even vehicle rollover in severe cases, a path-tracking strategy for differential steering autonomous vehicles considering active body inward tilt is proposed. Aiming at the problems of fixed parameters and insufficient adaptability of model predictive control in the path-tracking process of autonomous vehicles, this paper proposes a collaborative adaptive model predictive controller (MPC) with preview time and weight matrix based on fuzzy inference as the upper control, so as to realize the tracking control of the reference path by conventionally steered autonomous vehicles. In the lower control, an H∞/H2 hybrid controller with particle swarm optimization (PSO)-based parameter self-tuning is employed to control the differential steering autonomous vehicle (DSAV) to track the reference model, achieving differential steering and active body inward tilt simultaneously. Co-simulation results of CarSim and Simulink show that the proposed method outperforms the fixed-preview-time MPC and the manually tuned H∞/H2 hybrid controller. Compared with the latter, the maximum absolute values of lateral deviation and yaw angle deviation are reduced by 17.9% and 14.5%, respectively; the maximum deviation in the reference yaw rate is decreased by 21.2%; the maximum absolute value of the inward tilt angle is reduced by 53.4%; and the maximum values of LTR and occupant-perceived lateral acceleration are lowered by 57.1% and 44.2%, respectively. Full article
(This article belongs to the Special Issue Control Engineering and Artificial Intelligence)
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32 pages, 5732 KB  
Article
Multi-Objective Optimization of the Grinding Process in a Spring-Rotor Mill Using Regression-Based Modeling
by Aidos Baigunusov, Bekbolat Moldakhanov, Alina Kim, Mikhail Doudkin, Vladimir Yakovlev, Piotr Stryczek and Tadeusz Lesniewski
Machines 2026, 14(3), 356; https://doi.org/10.3390/machines14030356 - 23 Mar 2026
Viewed by 1011
Abstract
This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a [...] Read more.
This study addresses the problem of improving the efficiency of fine grinding of bulk materials in a spring-rotor mill. The objective is to determine technologically sound operating parameters based on mathematical modeling, design of experiments, and multi-objective optimization. The methodology relies on a full-factorial experimental design according to the Hartley plan, with five control factors: rotor rotational speed, material loading ratio, overlap of the working zones, grinding chamber clearance, and grinding duration. The analyzed responses include grinding fineness, throughput, power consumption, specific energy consumption, and specific metal intensity. Based on experimental data, adequate second-order polynomial regression models were obtained for all response variables using the least-squares method. Statistical analysis showed that grinding time and rotational speed had the most significant influence on the process. Multi-objective optimization using the weighted-sum method enabled the identification of optimal operating conditions that balance product quality, throughput, and energy consumption. Verification experiments confirmed the adequacy of the developed models. Practical implementation of the optimized regimes increases throughput by 15–20% while simultaneously reducing energy consumption by 8–12% compared with empirically selected operating conditions. The proposed models and recommendations provide a quantitative basis for tuning and controlling grinding equipment in processing industries. Full article
(This article belongs to the Section Material Processing Technology)
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19 pages, 4352 KB  
Article
Myoelectric Controlled Bionic Robotic Hand for Voluntary Finger Motion Driven by Neuromuscular Intent
by André Moreira, Marco Pinto, Miguel Fernandes, João Costa, Jorge Fidalgo and Alessandro Fantoni
Machines 2026, 14(3), 355; https://doi.org/10.3390/machines14030355 - 23 Mar 2026
Cited by 1 | Viewed by 1781
Abstract
Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the [...] Read more.
Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the forearm are processed via amplification, filtering, and digital analysis to enable accurate detection of muscle activity. The system achieves independent and simultaneous actuation of five fingers using a tendon-driven, servo-actuated mechanism in a lightweight ABS structure. Experimental evaluation demonstrates finger actuation delays ranging from 314 ms to 650 ms, maximum holding strengths between 1.75 N and 4.07 N, and minimum gripping distances between 22 mm and 49 mm across all five fingers, with peak motor currents remaining below 0.7 A. Results validate consistent muscle activity detection, successful execution of individual and combined finger movements, and the robustness of the proposed design. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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22 pages, 4091 KB  
Article
3D Trajectory Tracking Based on Super-Twisting Observer and Non-Singular Terminal Sliding Mode Control for Underactuated Autonomous Underwater Vehicle
by Zehui Yuan, Long He, Ya Zhang, Shizhong Li, Chenrui Bai and Zhuoyan Qi
Machines 2026, 14(3), 354; https://doi.org/10.3390/machines14030354 - 21 Mar 2026
Cited by 1 | Viewed by 781
Abstract
This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on [...] Read more.
This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on the kinematic and dynamic models of autonomous underwater vehicle, virtual velocity commands are constructed via backstepping approach to stabilize the position and attitude errors. To circumvent the “differential explosion” problem inherent in conventional backstepping control caused by repeated differentiations of virtual control variables, first-order low-pass filters are introduced to construct dynamic surface control, yielding smooth derivatives of virtual velocity commands. Secondly, to enhance convergence rate and robustness, a non-singular terminal sliding surface is designed at the dynamic level, and a terminal reaching law is formulated to achieve finite-time convergence of velocity tracking errors. Furthermore, to compensate for external disturbances and unmodeled dynamics, a disturbance observer based on the super-twisting algorithm is developed, enabling finite-time high-precision estimation of lumped disturbances, with the estimation results incorporated into the control law for feedforward compensation. Finally, comparative simulations are conducted under two typical disturbance scenarios. The results demonstrate that the proposed method achieves instantaneous disturbance estimation (reducing convergence time from 3 s to near zero), significantly smoother control inputs, and superior tracking accuracy with RMSE as low as 0.6788 m and MAE as low as 0.1468 m, reducing errors by up to 30.6% compared to baseline methods. Full article
(This article belongs to the Special Issue Mastering Vibrations: The Latest Breakthroughs in Control for Mechanical Systems)
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32 pages, 8316 KB  
Article
An Adaptive Enhancement Method for Weak Fault Diagnosis of Locomotive Gearbox Bearings Under Wheel–Raisl Excitation
by Yong Li, Wangcai Ding and Yongwen Mao
Machines 2026, 14(3), 353; https://doi.org/10.3390/machines14030353 - 21 Mar 2026
Cited by 1 | Viewed by 533
Abstract
Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, [...] Read more.
Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article
(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)
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37 pages, 2896 KB  
Article
Energy-Efficient Resilience Scheduling for Elevator Group Control via Queueing-Based Planning and Safe Reinforcement Learning
by Tingjie Zhang, Tiantian Zhang, Hao Zou, Chuanjiang Li and Jun Huang
Machines 2026, 14(3), 352; https://doi.org/10.3390/machines14030352 - 21 Mar 2026
Viewed by 967
Abstract
High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs [...] Read more.
High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs and carbon constraints sharpen the tension between peak-power control, energy savings, and service capacity. This paper proposes a two-layer resilience scheduling framework that integrates queueing-based planning with safe reinforcement learning (RL) fine-tuning. In the planning layer, parsimonious queueing approximations and scenario-based evaluation construct a finite set of implementable mode cards and emergency switching cards; Sample Average Approximation (SAA) combined with Conditional Value-at-Risk (CVaR) constraints filter candidates to enforce tail-risk-aware service limits while keeping power demand within a prescribed envelope. In the execution layer, online dispatch is formulated as a constrained Markov decision process; within the planning layer limits, action masking and Lagrangian safe RL learn small adaptive adjustments to suppress tail-waiting risk and improve recovery dynamics without increasing peak-power commitments. The experiments under morning peaks and post-event surges confirm tail risk reduction and accelerated recovery. For partial outages, the framework prioritizes SLA coverage and recovery speed, accepting a bounded increase in tail risk as a manageable trade-off. Throughout all tests, peak power remains within the prescribed limits. Improvements persist across random seeds and demand fluctuations, indicating distributional robustness and cross-scenario generalization. Ablation studies further reveal complementary roles: removing the planning layer CVaR screening worsens tail performance, while removing the execution layer action masking increases constraint violations and destabilizes recovery. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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25 pages, 8662 KB  
Article
A Simple Comparative Study on the Effectiveness of Bearing Fault Detection Using Different Sensors on a Roller Bearing
by Haobin Wen, Khalid Almutairi and Jyoti K. Sinha
Machines 2026, 14(3), 351; https://doi.org/10.3390/machines14030351 - 20 Mar 2026
Viewed by 1273
Abstract
Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and [...] Read more.
Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and measurement technologies is essential. This paper presents a comparative study on the applications of four sensor types in bearing condition monitoring. These four sensor types are vibration accelerometer, encoder, acoustic emission (AE) sensor and motor current probe. Their effectiveness and practicability in bearing fault detection are evaluted. Data simultaneously measured from these four sensor types on a split roller bearing within an experimental rig are used for the analysis. Different factors such as machine operating speeds, bearing fault sizes and their location are considered during the experiments to understand the effectiveness and fault detectability of different sensors on a common bearing. Both the accelerometer and the AE sensor are observed to effectively detect all bearing faults from small to extended sizes and from low to high operating speeds. However, the other two sensors, the encoder and motor current probe, have been found to be sensitive only to relatively larger fault sizes and higher operating speeds. The study presents valuable insights into their advantages and limitations through a systematic comparison of roller bearing fault detection. The study provides a basis for sensor selection in bearing condition monitoring and fault detection to enhance the reliability of industrial maintenance activities. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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28 pages, 4866 KB  
Article
Trajectory Optimization with Feasibility Guidance for Agile UAV Path Planning Under Geometric Constraints
by Shoshi Kawarabayashi, Kenji Uchiyama and Kai Masuda
Machines 2026, 14(3), 350; https://doi.org/10.3390/machines14030350 - 20 Mar 2026
Viewed by 1351
Abstract
This paper presents a practical optimization framework for improving trajectory feasibility in constrained nonlinear optimal control problems for agile unmanned aerial vehicles (UAVs). The proposed method addresses trajectory optimization problems with non-convex geometric constraints, where gradient-based solvers often fail to converge to feasible [...] Read more.
This paper presents a practical optimization framework for improving trajectory feasibility in constrained nonlinear optimal control problems for agile unmanned aerial vehicles (UAVs). The proposed method addresses trajectory optimization problems with non-convex geometric constraints, where gradient-based solvers often fail to converge to feasible solutions. The framework combines Model Predictive Path Integral (MPPI) control and the Augmented Lagrangian iterative Linear Quadratic Regulator (AL-iLQR). MPPI is employed as a fast sampling-based guidance mechanism to explore feasible regions of the trajectory space, while AL-iLQR is used to efficiently refine locally optimal solutions with high numerical accuracy. By decoupling feasibility exploration from local optimal refinement, the proposed method mitigates the sensitivity of gradient-based trajectory optimization to initialization in highly constrained environments. Numerical simulations involving both simplified two-dimensional dynamics and full quadrotor models demonstrate that the proposed approach significantly improves the probability of converging to feasible and dynamically consistent trajectories compared with AL-iLQR alone. The proposed method does not aim to provide theoretical guarantees of global optimality; instead, it offers a practical and computationally efficient strategy for enhancing feasibility and robustness in real-time UAV trajectory optimization. Full article
(This article belongs to the Special Issue Flight Control and Path Planning of Unmanned Aerial Vehicles)
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23 pages, 3937 KB  
Article
Deep Learning-Enhanced Fault Detection and Localization in Induction Motor Drives: A ResMLP and TCN Framework
by Hamza Adaika, Khaled Laadjal, Zoheir Tir and Mohamed Sahraoui
Machines 2026, 14(3), 349; https://doi.org/10.3390/machines14030349 - 20 Mar 2026
Viewed by 853
Abstract
Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances (Za,Zb,Zc) and detection [...] Read more.
Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances (Za,Zb,Zc) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article
(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)
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27 pages, 4265 KB  
Article
Condition Monitoring Model Development for Belt Systems Using Hybrid CNN–BiLSTM Deep-Learning Techniques
by Mortda Mohammed Sahib, Philipp Plänitz, Matthias Hackert-Oschätzchen and Christoph Lerez
Machines 2026, 14(3), 348; https://doi.org/10.3390/machines14030348 - 19 Mar 2026
Cited by 1 | Viewed by 1173
Abstract
Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A [...] Read more.
Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A combined Convolutional Neural Network with Bi-directional Long Short-Term Memory (CNN-BiLSTM) model is assigned for processing the operational parameters along with vibrational signals to predict belt-drive system conditions in two separate binary classifications: faulty or healthy and unbalanced or balanced conditions. The data flow in the CNN-BiLSTM model initiates with the CNN to extract the features from the vibration signals and performs essential pattern detection. Consequently, the BiLSTM’s role is to capture long-term temporal relationships that cannot be captured by the CNN alone. To predict the targeted conditions, a fully connected layer with a classifier is built at the BiLSTM outputs. For efficient model training, the data is preprocessed through denoising, augmentation, and normalization. Additionally, hyperparameter tuning is conducted to explore different model configurations and select the optimal ones on the basis of relevant performance. An ablation study is conducted to investigate the use of CNN and BiLSTM models individually, confirming that combining both components is essential for accurate classification. Moreover, the cross-validation technique is used to assess the proposed model’s generality by organizing unseen data across rotational speeds, which also depicts robust performance under varying operating conditions. The key added value of this study lies in integrating deep-learning techniques to address a knowledge gap by using raw vibrational signals to establish intelligent monitoring systems, which represents a new scientific contribution to the predictive maintenance of belt-drive systems. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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40 pages, 6534 KB  
Article
Telehandler Stability Analysis Using a Virtual Tilt & Rotation Platform
by Beatriz Puras, Gustavo Raush, Germán Filippini, Javier Freire, Pedro Roquet, Manel Tirado, Oriol Casadesús and Esteve Codina
Machines 2026, 14(3), 347; https://doi.org/10.3390/machines14030347 - 19 Mar 2026
Viewed by 947
Abstract
This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches [...] Read more.
This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article
(This article belongs to the Section Vehicle Engineering)
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15 pages, 1792 KB  
Article
Developing a Digital Twin for Human Performance Assessment in Human–Machine Interaction
by Erik Novak, Aljaž Javernik, Iztok Palčič and Robert Ojsteršek
Machines 2026, 14(3), 346; https://doi.org/10.3390/machines14030346 - 19 Mar 2026
Cited by 1 | Viewed by 1392
Abstract
Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine [...] Read more.
Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine interaction. A physical system comprising a conveyor belt, sensors, and operator-controlled elements was constructed, and a functionally equivalent digital model was created using Arduino IDE and MATLAB/Simulink. The digital twin records and synchronizes key human–machine interaction variables, including response time, assembly time, and execution consistency. Validation was conducted through simulation testing and an experimental study with 18 participants performing repeated assembly cycles. The results show that the developed digital twin accurately replicates the temporal dynamics of the physical process and reliably captures individual human performance patterns. Overall, the study provides a validated methodological framework for human–machine-integrated digital twins and demonstrates their potential for analyzing human–machine interaction, supporting operator training, and adaptive workplace design in line with Industry 5.0 principles. Full article
(This article belongs to the Special Issue Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems)
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1 pages, 128 KB  
Correction
Correction: Qie et al. Underwater Objective Detection Algorithm Based on YOLOv8-Improved Multimodality Image Fusion Technology. Machines 2025, 13, 982
by Yage Qie, Chao Fang, Jinghua Huang, Donghao Wu and Jian Jiang
Machines 2026, 14(3), 345; https://doi.org/10.3390/machines14030345 - 19 Mar 2026
Viewed by 322
Abstract
In the original publication [...] Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
20 pages, 4795 KB  
Article
Effect of Combined Film Cooling and Swirl on the Thermal Performance of a Contoured High Pressure Turbine Vane of a Modern Turbofan Engine: A Numerical Study
by Djihane Mazouz, Zakaria Mansouri and Salaheddine Azzouz
Machines 2026, 14(3), 344; https://doi.org/10.3390/machines14030344 - 18 Mar 2026
Viewed by 717
Abstract
Modern high-pressure turbine (HPT) nozzle guide vanes (NGVs) operate under non-uniform inlet conditions, including hot streaks and swirl, which can induce complex flow phenomena and uneven thermal loading. These effects, particularly at the hub-vane corner, can compromise NGV durability, yet the combined influence [...] Read more.
Modern high-pressure turbine (HPT) nozzle guide vanes (NGVs) operate under non-uniform inlet conditions, including hot streaks and swirl, which can induce complex flow phenomena and uneven thermal loading. These effects, particularly at the hub-vane corner, can compromise NGV durability, yet the combined influence of swirl and film cooling remains underexplored. The objective of this study is to investigate the aerothermal behaviour of contoured first-stage NGVs under varying swirl intensities and directions to improve understanding of hub and corner thermal protection and failure mechanisms. Steady, compressible RANS simulations were conducted with the k-ω SST turbulence model. A vane with a contoured hub and multiple film cooling rows was designed and analysed under axial and swirling inflows, both clockwise and counter-clockwise, with swirl numbers of Sn = ±0.2 and ±0.4. Axial flow achieved the highest area-averaged film cooling effectiveness (FCE) of 0.617. Negative swirl (Sn = −0.4) improved suction-side corner FCE to 0.215 but reduced pressure-side cooling, whereas positive swirl (Sn = 0.4) improved pressure-side cooling but reduced suction-side FCE to 0.043. Corner temperatures under positive swirl reached 1780 K, consistent with promoting failure, while negative swirl reduced corner temperatures to 1516 K. Aerodynamic losses increased with swirl, with negative swirl generating 5.78% higher total pressure losses than the axial baseline. Swirl altered the corner vortex topology, affecting boundary layer interactions and local heat transfer. These results highlight a trade-off between thermal protection and aerodynamic efficiency, emphasising that optimising NGV performance requires careful design of hub cooling and consideration of swirl direction and intensity. Full article
(This article belongs to the Section Turbomachinery)
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25 pages, 4865 KB  
Article
Hybrid Attention-Augmented Deep Reinforcement Learning for Intelligent Machining Process Route Planning
by Ruizhe Wang, Minrui Wang, Ziyan Du, Xiaochuan Dong and Yibing Peng
Machines 2026, 14(3), 343; https://doi.org/10.3390/machines14030343 - 18 Mar 2026
Cited by 1 | Viewed by 1040
Abstract
Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established [...] Read more.
Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article
(This article belongs to the Section Advanced Manufacturing)
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17 pages, 3940 KB  
Article
Unsteady Internal Flow and Cavitation Characteristics of a Hydraulic Dynamometer for Measuring High-Power Gas Turbines
by Ye Yuan, Zhenyang Liu and Qirui Chen
Machines 2026, 14(3), 342; https://doi.org/10.3390/machines14030342 - 18 Mar 2026
Viewed by 735
Abstract
Hydraulic dynamometer is the key equipment to measure the dynamic performance of high-power gas turbines and steam, with its internal flow characteristics directly influencing measurement accuracy and service life. This paper focuses on the power absorption performance and internal flow characteristics of a [...] Read more.
Hydraulic dynamometer is the key equipment to measure the dynamic performance of high-power gas turbines and steam, with its internal flow characteristics directly influencing measurement accuracy and service life. This paper focuses on the power absorption performance and internal flow characteristics of a hydraulic dynamometer with perforated-disk rotor. A hydraulic test platform is established to measure the power absorption performance of megawatt-level hydraulic dynamometers. When the rotor speed reaches a certain value under the full-water condition, the power absorption of the hydraulic dynamometer reaches its limit. Numerical simulations are applied to study the internal flow characteristics and cavitation evolution features of the perforated-disk-type hydraulic dynamometer. The flow within the outermost rotor pores is the primary factor influencing unsteady flow behaviour, with dynamic–static interference playing a key role in inducing flow excitation. Moreover, cavitation mainly occurs in the flow passages of the end rotor and the outermost flow pores of the middle rotor, where the development and collapse of cavitation bubbles lead to flow instability. As the rotation speed decreases, the power absorption performance significantly decreases under cavitation conditions. These findings provide a theoretical basis for the structural optimization and engineering application of high-power hydraulic dynamometers. Full article
(This article belongs to the Special Issue Advanced Research and Development in Fluid Machinery: Design, Optimization, and Applications)
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24 pages, 5290 KB  
Article
A Unified Framework for Load Capacity Optimization and Compliant Cooperative Manipulation of Dual Wheeled Mobile Manipulators
by Hongjun Xing, Yundong Fu, Yanqing Liu, Yuqi Yang and Jinbao Chen
Machines 2026, 14(3), 341; https://doi.org/10.3390/machines14030341 - 18 Mar 2026
Viewed by 1180
Abstract
Flexible and safe object handling in modern industrial environments increasingly relies on mobile robotic systems capable of both dexterous manipulation and adaptive motion. However, when wheeled mobile manipulators (WMMs) operate under heavy or dynamically varying loads, challenges arise in maintaining sufficient force exertion [...] Read more.
Flexible and safe object handling in modern industrial environments increasingly relies on mobile robotic systems capable of both dexterous manipulation and adaptive motion. However, when wheeled mobile manipulators (WMMs) operate under heavy or dynamically varying loads, challenges arise in maintaining sufficient force exertion capability and achieving stable coordination, particularly during cooperative transportation. In this paper, we present a unified framework to address these challenges with three main contributions. A quadratic-programming-based redundancy resolution scheme incorporating a load-capacity maximization metric is developed to explicitly enhance the force exertion capability of the system under heavy loads. A variable-admittance cooperative control strategy for dual-WMM transport is proposed to ensure synchronized motion and adaptive force regulation during collaborative manipulation. In addition, a unified framework that integrates configuration optimization with compliant cooperative control is established, enabling strict constraint enforcement, improved load capacity, and robust coordination between the two WMMs. Extensive simulations demonstrate the effectiveness of the proposed methods in improving load-handling performance and ensuring coordinated, compliant cooperative manipulation. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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19 pages, 4836 KB  
Article
Design and Performance Analysis of an XY Precision Motion Platform with Decoupling Based on Connecting Arm and Guide Rail Integration
by Junjie Jiang, Yi Fang, Xulichen Feng, Jiahao Zhang, Hongyang Cui, Liangkun Lu, Zirui Zhao and Zhiling Xiao
Machines 2026, 14(3), 340; https://doi.org/10.3390/machines14030340 - 18 Mar 2026
Viewed by 1003
Abstract
To address the trade-off between macroscopic stroke and high stiffness in XY precision positioning, this study proposes a platform utilizing a Connecting Arm and Guide Rail Integration decoupling mechanism. Distinct from traditional suspended-mover or flexure-based schemes, this design integrates precision guiding directly into [...] Read more.
To address the trade-off between macroscopic stroke and high stiffness in XY precision positioning, this study proposes a platform utilizing a Connecting Arm and Guide Rail Integration decoupling mechanism. Distinct from traditional suspended-mover or flexure-based schemes, this design integrates precision guiding directly into a rigid connecting arm to mechanically isolate parasitic motion. Finite Element Analysis confirms a safety margin with a maximum equivalent stress of 8.34 MPa. Notably, the platform achieves a first-order natural frequency of 864.82 Hz, which is significantly higher than the Voice Coil Motor’s actuation frequency, effectively mitigating low-frequency resonance. Transient dynamic analysis further suggests that the mechanism suppresses cross-axis interference to the sub-nanometer level (0.61 nm) during high-acceleration maneuvers. Experimental validation demonstrates favorable tracking capabilities: for a 5 mm step motion, the positioning deviation is controlled within 0.005 mm. These findings suggest that the proposed solution offers a feasible and competitive approach for high-stiffness precision positioning applications. Full article
(This article belongs to the Section Automation and Control Systems)
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18 pages, 8749 KB  
Article
Biomechanical and Signal-Based Characterization of Karate Lateral Kicks Using Videogrammetry Analysis
by Luis Antonio Aguilar-Pérez, Jorge Luis Rojas-Arce, Luis Jímenez-Ángeles, Carlos Alberto Espinoza-Garces, Adolfo Ángel Casarez-Duran and Christopher René Torres-SanMiguel
Machines 2026, 14(3), 339; https://doi.org/10.3390/machines14030339 - 17 Mar 2026
Viewed by 1419
Abstract
Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical [...] Read more.
Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training. Full article
(This article belongs to the Special Issue Selected Papers from the 8th International Symposium on Multibody Systems and Mechatronics)
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24 pages, 8720 KB  
Article
Research on the Influence of Structural Parameters on the Mechanical Performance of Crane Slewing Bearings
by Yingjia Wang, Hongshuo Yan, Fei Li, Tianxi Wang and Yuanyuan Li
Machines 2026, 14(3), 338; https://doi.org/10.3390/machines14030338 - 17 Mar 2026
Viewed by 1083
Abstract
Slewing bearing is a rotating component with high load-carrying capacity, which is an important part of the crane connecting the upper rotating parts and the lower supporting parts; therefore, it is of great significance to analyze the performance of slewing bearings. This paper [...] Read more.
Slewing bearing is a rotating component with high load-carrying capacity, which is an important part of the crane connecting the upper rotating parts and the lower supporting parts; therefore, it is of great significance to analyze the performance of slewing bearings. This paper establishes a theoretical model and an integrated finite element model for the mechanical performance of slewing bearings, and the results of the two show high consistency. The influences of four bearing parameters (contact angle, raceway curvature radius coefficient, rolling element diameter, and number of rolling elements) and three bolt parameters (number of bolts, bolt preload, and washer thickness) on the mechanical performance of the slewing bearing were studied, aiming to provide a reference basis for the selection and design of crane slewing bearings. Full article
(This article belongs to the Section Machine Design and Theory)
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17 pages, 4901 KB  
Article
A New Portable Smart Percussion System Embedded on Raspberry Pi for Bolt Looseness Detection
by Weiliang Zheng, Duanhang Zhang, Keyu Du and Furui Wang
Machines 2026, 14(3), 337; https://doi.org/10.3390/machines14030337 - 16 Mar 2026
Viewed by 837
Abstract
Bolted joints are extensively used in a wide range of industrial and commercial structures, making their condition monitoring essential for ensuring structural integrity and operational safety. Under the influence of vibration, cyclic loading, and environmental factors, bolts may gradually lose preload, which can [...] Read more.
Bolted joints are extensively used in a wide range of industrial and commercial structures, making their condition monitoring essential for ensuring structural integrity and operational safety. Under the influence of vibration, cyclic loading, and environmental factors, bolts may gradually lose preload, which can degrade joint stiffness and eventually lead to structural failure. To address this issue, this study presents a smart percussion system developed on a Raspberry Pi platform that integrates acoustic signal acquisition, real-time signal processing, and visualization of diagnostic results. A bolt looseness detection strategy combining audio feature extraction with unsupervised learning is proposed. In contrast to traditional percussion-based approaches that depend on supervised learning and predefined baseline datasets, the proposed method does not require prior reference data, significantly improving its adaptability and ease of deployment across different structures, which shows essential practical significance. Experimental investigations demonstrate the effectiveness and advantages of the proposed system, indicating its strong potential to enhance percussion-based bolt looseness detection and to support real-time structural health monitoring, which are real-world engineering applications. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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25 pages, 2220 KB  
Article
HRC Metrology: Assessment Criteria, Metrics and Methods for Human–Robot Co-Manipulation Tasks
by S. M. Mizanoor Rahman
Machines 2026, 14(3), 336; https://doi.org/10.3390/machines14030336 - 16 Mar 2026
Cited by 1 | Viewed by 1412
Abstract
We developed a human–robot collaborative manipulation system (co-manipulation system) in the form of a power assist robotic system (PARS) where a human and a robot collaborated to perform the co-manipulation of an object with power assistance. We conducted an experiment (the first experiment), [...] Read more.
We developed a human–robot collaborative manipulation system (co-manipulation system) in the form of a power assist robotic system (PARS) where a human and a robot collaborated to perform the co-manipulation of an object with power assistance. We conducted an experiment (the first experiment), where in each trial of the experiment, a human subject performed the co-manipulation of the object with the PARS, and an expert human–robot co-manipulation researcher observed the co-manipulation task. We collected the co-manipulation and observation data, analyzed the data, and conducted reviews of the related literature, and developed the HRC (human–robot collaboration) metrology, which consisted of necessary criteria, metrics and methods to assess human–robot collaborative manipulation tasks. The proposed HRC metrology consisted of both human–robot collaborative performance and human–robot interactions (HRI) related assessment criteria. Then, we developed another human–robot co-manipulation system using a robot manipulator. In this system, the human–robot co-manipulation task was performed in conjunction with a collaborative assembly task between the robot and human co-workers. In another experiment (the second experiment), we assessed the co-manipulation task for each robotic system separately based on the developed HRC metrology (set of assessment criteria, metrics and methods) to verify and validate the practicality, usability and effectiveness of the criteria, metrics and methods. The results showed that the HRC metrology was effective and practical in assessing the co-manipulation tasks. We then discussed the strengths and limitations of the assessment criteria, metrics and methods. The proposed HRC metrology can be used to assess human–robot collaborative performance and human–robot interactions in human–robot co-manipulation tasks with potential real-world applications in industrial manipulation and manufacturing, transport, logistics, civil construction, rescue and disaster management, timber processing, etc. Full article
(This article belongs to the Special Issue Design and Control of Assistive Robots)
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25 pages, 1694 KB  
Article
Tool-Health Digital Twin for CNC Predictive Maintenance via Innovation-Adaptive Sensor Fusion and Uncertainty-Aware Prognostics
by Zhuming Cao, Lihua Chen, Chunhui Li, Laifa Zhu and Zhengjian Deng
Machines 2026, 14(3), 335; https://doi.org/10.3390/machines14030335 - 16 Mar 2026
Cited by 2 | Viewed by 1888
Abstract
A tool-health digital twin for CNC predictive maintenance is developed and operationalised as a fusion-and-state-estimation core that produces a latent tool-health trajectory (wear level and wear-rate dynamics) from multi-rate sensor streams for diagnosis and remaining useful life (RUL) forecasting under strict edge latency [...] Read more.
A tool-health digital twin for CNC predictive maintenance is developed and operationalised as a fusion-and-state-estimation core that produces a latent tool-health trajectory (wear level and wear-rate dynamics) from multi-rate sensor streams for diagnosis and remaining useful life (RUL) forecasting under strict edge latency constraints. The scope is tool-health–informed maintenance decisions (condition-based tool replacement/scheduling), rather than a comprehensive maintenance twin for all CNC subsystems. Multi-rate vibration, spindle-current, and temperature signals are synchronized and windowed, and a linear state-space model with Kalman filtering and innovation-guided adaptive noise estimation stabilizes the latent health state across operating-regime changes. The fused state is then used by compact sequence learners, an LSTM for edge feasibility, and a compact Transformer as a higher-accuracy comparison, to output fault categories and RUL estimates. Predictive uncertainty is quantified via a Monte Carlo dropout and linked to reliability-aware actions through a simple alarm/defer/schedule policy, while SHAP provides feature-level interpretability. On a CNC testbed, fusion improves fault F1 from 0.811 to 0.892 and PR-AUC from 0.867 to 0.918 while reducing RUL RMSE from 10.4 to 8.1 cycles; the compact Transformer reaches 0.903 F1 and 7.9-cycle RMSE at higher inference time. The end-to-end pipeline remains within a ≤100 ms breakdown, maintains in-band innovation statistics, supports rehearsal-based updates under drift, and is additionally evaluated on external tool-wear and turbofan datasets. Full article
(This article belongs to the Section Advanced Manufacturing)
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22 pages, 5879 KB  
Article
An Obstacle-Negotiation Wheel with Hybrid Active–Passive Mechanism for Mechanical Augmentation
by Peixiang Wang, Xinyuan Wen, Hongjun Yin, Meiru Li and Pingyi Liu
Machines 2026, 14(3), 334; https://doi.org/10.3390/machines14030334 - 16 Mar 2026
Viewed by 2068
Abstract
To address the limitation of wheeled mobile robots in traversing unstructured terrain, this paper proposes an Active–Passive Hybrid Obstacle-Crossing Wheel (APHOCW). The mechanism integrates an active angle-adjustment mechanism and a lever-assist mechanism. While maintaining low system complexity and high reliability, it utilizes periodically [...] Read more.
To address the limitation of wheeled mobile robots in traversing unstructured terrain, this paper proposes an Active–Passive Hybrid Obstacle-Crossing Wheel (APHOCW). The mechanism integrates an active angle-adjustment mechanism and a lever-assist mechanism. While maintaining low system complexity and high reliability, it utilizes periodically telescoping assist levers that rotate with the wheel to overcome obstacles. By actively adjusting the eccentric angle, the trajectory of the assist levers can be modified to optimize the crossing posture. Through geometric and quasi-static mechanical modeling, dynamic simulation, and prototype experiments, this study systematically validated the robot’s obstacle-crossing capability and continuous step-climbing performance under different eccentric angles. Simulation and experimental results demonstrate that in the lever-assisted obstacle-crossing mode, the robot can stably overcome obstacles with a height up to 2.1 times its wheel radius and accomplish continuous step ascent. A smaller eccentric angle helps increase the maximum obstacle-crossing height, while a larger eccentric angle exhibits superior energy efficiency under sufficient ground-friction conditions. Full article
(This article belongs to the Special Issue The Kinematics and Dynamics of Mechanisms and Robots)
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41 pages, 10075 KB  
Article
Deep Deterministic Policy Gradient-Based Actor–Critic Reinforcement Learning for Torque Ripple Minimization in Switched Reluctance Motors
by Divya Ramasamy and Sundaram Maruthachalam
Machines 2026, 14(3), 333; https://doi.org/10.3390/machines14030333 - 16 Mar 2026
Cited by 2 | Viewed by 1077
Abstract
The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating [...] Read more.
The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article
(This article belongs to the Section Electrical Machines and Drives)
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23 pages, 3875 KB  
Article
Design, Analysis, and Prototyping of a Formula SAE Application Flux-Switching Permanent Magnet Motor
by Filippo Crescenzio and Nicola Bianchi
Machines 2026, 14(3), 332; https://doi.org/10.3390/machines14030332 - 15 Mar 2026
Cited by 1 | Viewed by 1277
Abstract
This paper investigates the design, analysis, and prototyping of a Flux-Switching Permanent Magnet (FSPM) motor for Formula SAE electric vehicle applications. The stringent competition requirements demand traction motors with high torque and power density, and reliable operation at elevated speeds. An analytical model [...] Read more.
This paper investigates the design, analysis, and prototyping of a Flux-Switching Permanent Magnet (FSPM) motor for Formula SAE electric vehicle applications. The stringent competition requirements demand traction motors with high torque and power density, and reliable operation at elevated speeds. An analytical model based on air-gap permeance and magnetomotive force distributions is developed to provide an effective preliminary design tool and to reduce computational effort. The proposed model is used to define the main geometrical parameters of a 12-slot, 10-rotor-tooth FSPM machine, which is subsequently validated through finite element analysis. Analytical and numerical results are compared in terms of air-gap flux density, flux linkage, and torque capability, showing good agreement. Manufacturing-driven design choices, including optimized magnet slot geometry, laminated permanent magnets for eddy-current loss mitigation, and a mechanically robust lightweight rotor, are introduced to ensure high-speed operability and assembly reliability. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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19 pages, 3750 KB  
Article
Toward Automated Detection of Permanent Magnet Motors in WEEE Recycling Using Discriminative Transfer Learning
by Niccolò Pezzati, Maurizio Guadagno, Lorenzo Berzi and Massimo Delogu
Machines 2026, 14(3), 331; https://doi.org/10.3390/machines14030331 - 15 Mar 2026
Viewed by 1424
Abstract
Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the [...] Read more.
Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the collection phase. In this context, this work proposes the integration of an image-based classification framework within the Waste Electrical and Electronic Equipment (WEEE) recycling pipeline to selectively identify electric motors containing permanent magnets (PMs) and direct them toward dedicated recycling processes for rare earth recovery. The proposed methodology relies on a Discriminative Transfer Learning (DTL) approach based on a ResNeXt convolutional neural network (CNN), adapted to a proprietary and heterogeneous dataset of electric motors acquired in an industrial recycling facility. The objective is twofold: first, to identify motors containing PMs; second, to classify motors into construction categories according to their likelihood of incorporating PMs. Experimental results show promising performance in terms of PM-containing motor detection capability, establishing a robust foundation for the automated recovery of REEs at an industrial scale. Furthermore, the model’s generalization capabilities can be further enhanced through the expansion of collaborative datasets and the integration of advanced scanning technologies. Full article
(This article belongs to the Section Industrial Systems)
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25 pages, 3675 KB  
Article
Natural Motion Design for Energy-Efficient Pick-and-Place Scenarios
by Juan Pablo Mora, Carlos F. Rodriguez and Burkhard Corves
Machines 2026, 14(3), 330; https://doi.org/10.3390/machines14030330 - 14 Mar 2026
Cited by 1 | Viewed by 856
Abstract
Reducing the energy consumption of industrial robots performing pick-and-place tasks is required to increase profitability while reducing carbon footprint. Natural motion stands out as a mixed-energy-reduction strategy, especially useful for cyclical tasks. An optimization approach is proposed for calculating the elastic parameters, namely [...] Read more.
Reducing the energy consumption of industrial robots performing pick-and-place tasks is required to increase profitability while reducing carbon footprint. Natural motion stands out as a mixed-energy-reduction strategy, especially useful for cyclical tasks. An optimization approach is proposed for calculating the elastic parameters, namely the stiffness and equilibrium position, of constant-stiffness springs parallel to the actuators of parallel robots. Three typical trajectory-dependent methods for calculating these parameters are presented: free-vibration response, optimized, and predefined trajectory. As the set of springs and the task specification are strongly coupled, deviations from the nominal task would require replacing or removing the springs. Therefore, two adjustment strategies, one based on trajectory optimization and the other on equilibrium position update, are proposed to further exploit the natural motion. All optimization problems are solved and compared in a case study of a five-bar linkage performing a nominal pick-and-place task. Then, a palletizing pick-and-place scenario is introduced to perform the proposed trajectory and equilibrium adjustments. It is shown that using nominal springs reduces energy consumption near the nominal task, and implementing the proposed adjustments reduces energy over a wider region. Full article
(This article belongs to the Special Issue Selected Papers from the 8th International Symposium on Multibody Systems and Mechatronics)
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37 pages, 10880 KB  
Article
Predicting Part Orientation Distributions in Linear Feeders Using Simulation-Driven Deep Learning
by Idan Zucker and Chen Giladi
Machines 2026, 14(3), 329; https://doi.org/10.3390/machines14030329 - 13 Mar 2026
Viewed by 1018
Abstract
Designing linear conveyor feeders with passive fences for automated part orientation remains largely trial and error because the final orientation distribution is difficult to predict reliably before physical testing. We present a simulation-driven deep learning pipeline that predicts the full distribution of final [...] Read more.
Designing linear conveyor feeders with passive fences for automated part orientation remains largely trial and error because the final orientation distribution is difficult to predict reliably before physical testing. We present a simulation-driven deep learning pipeline that predicts the full distribution of final in-plane orientations for extruded, z-axis-symmetric parts interacting with linear feeders containing up to two straight or curved fences. Using Bullet physics-based simulation in CoppeliaSim, we generate 1048 main part–feeder samples across 38 part geometries, plus 78 fence generalization and 110 unseen part samples for a total of 1236 (41 unique parts), and train regression networks and a Variational Autoencoder, or VAE, to predict 360-bin orientation probability distributions. On known parts, the regression model achieves high accuracy on held-out test configurations, R2 on circular CDFs =0.97±0.05, and on unseen fence combinations, R2 on circular CDFs = 0.89 ± 0.11. Generalization to previously unseen part geometries is more challenging, with R2 on circular CDFs = 0.75 ± 0.18, indicating that geometric representation and dataset diversity are primary limitations. We also evaluate VAE reconstruction on datasets generated from simulations at different iteration counts: 5–100% of 1000 iterations in 5% increments. While within-level reconstruction remains high, cross-convergence evaluation shows that partial-iteration PMFs are far from fully converged labels in this dataset (overall CDF R2 = 0.01 at 5%, 0.32 at 50%, and 0.87 at 75%), so reduced-iteration simulations do not substitute for full convergence here. Overall, the proposed approach provides a data-driven foundation for feeder analysis and design, with future work focusing on improved geometric generalization and physical validation for industrial deployment. Full article
(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)
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12 pages, 3539 KB  
Article
Cyclic Torsional Behavior of 3D-Printed ABS: Role of Infill Density and Raster Orientation
by Grayson Lumsden, Jeremy Sarpong and Khalil Khanafer
Machines 2026, 14(3), 328; https://doi.org/10.3390/machines14030328 - 13 Mar 2026
Viewed by 877
Abstract
This study investigates the fatigue behavior of 3D-printed ABS subjected to cyclic torsional loads, with a focus on the effects of infill density and raster angle on torsional fatigue performance. A total of 50 test specimens representing 25 unique combinations of infill density [...] Read more.
This study investigates the fatigue behavior of 3D-printed ABS subjected to cyclic torsional loads, with a focus on the effects of infill density and raster angle on torsional fatigue performance. A total of 50 test specimens representing 25 unique combinations of infill density (20%, 40%, 60%, 80%, 100%) and raster angle (25°/−65°, 45°/−45°, 75°/−15°, 0°/90°) were fabricated and tested using the cyclic torsion system. Fatigue failure was defined as a 75% reduction in torsional strength, recorded through cycle-by-cycle torque monitoring. The twist angle was cyclically varied between ±10° at a frequency of 5 Hz until failure occurred. The results indicate that increasing infill density significantly improves fatigue life by reducing internal porosity and enhancing load transfer, with the greatest gains observed at high infill levels (≥80%). Raster angle has a minimal effect at low infill densities but becomes critical at higher densities, where optimized filament orientations substantially extend fatigue life. Intermediate raster angles, particularly 25° and 75°, outperform orthogonal layouts by enabling better stress redistribution and inter-layer load sharing, while a 90° orientation leads to premature failure due to stress concentration and inter-layer debonding. When normalized by mass, specimens with 100% infill and intermediate raster angles achieve the highest fatigue endurance, highlighting the synergistic role of infill density and raster orientation in optimizing the durability and mass efficiency of 3D-printed components under cyclic torsional loading. Full article
(This article belongs to the Section Advanced Manufacturing)
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