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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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23 pages, 4770 KB  
Article
Co-Design of Structural Parameters and Motion Planning in Serial Manipulators via SAC-Based Reinforcement Learning
by Yifan Zhu, Jinfei Liu, Hua Huang, Ming Chen and Jindong Qu
Machines 2026, 14(2), 158; https://doi.org/10.3390/machines14020158 - 30 Jan 2026
Viewed by 465
Abstract
In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based [...] Read more.
In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article
(This article belongs to the Section Machine Design and Theory)
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28 pages, 5671 KB  
Article
Analysis of Kinematic Crosstalk in a Four-Legged Parallel Kinematic Machine
by Giuseppe Mangano, Marco Carnevale and Hermes Giberti
Machines 2026, 14(2), 152; https://doi.org/10.3390/machines14020152 - 29 Jan 2026
Viewed by 370
Abstract
Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These [...] Read more.
Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 m/s2, exceeding the vestibular perception threshold of 0.17 m/s2 defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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22 pages, 2039 KB  
Article
A Machine Learning Framework for the Prediction of Propeller Blade Natural Frequencies
by Nícolas Lima Oliveira, Afonso Celso de Castro Lemonge, Patricia Habib Hallak, Konstantinos G. Kyprianidis and Stavros Vouros
Machines 2026, 14(1), 124; https://doi.org/10.3390/machines14010124 - 21 Jan 2026
Viewed by 606
Abstract
Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design [...] Read more.
Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves R2>0.90 and NRMSE<0.08 for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article
(This article belongs to the Section Turbomachinery)
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25 pages, 20803 KB  
Article
Hierarchical Path Planning for Automatic Parking in Constrained Scenarios via Entry-Point Guidance
by Liang Chen, Lizhi Huang, Chaoyi Chen, Guangwei Wang, Yougang Bian, Mengchi Cai, Qingwen Meng, Qing Xu, Jianqiang Wang and Keqiang Li
Machines 2026, 14(1), 112; https://doi.org/10.3390/machines14010112 - 18 Jan 2026
Viewed by 354
Abstract
Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search [...] Read more.
Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles. Full article
(This article belongs to the Special Issue Optimization-Based Motion Planning & Control for Autonomous Driving in Dynamic Environments)
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15 pages, 4459 KB  
Article
Automated Custom Sunglasses Frame Design Using Artificial Intelligence and Computational Design
by Prodromos Minaoglou, Anastasios Tzotzis, Klodian Dhoska and Panagiotis Kyratsis
Machines 2026, 14(1), 109; https://doi.org/10.3390/machines14010109 - 17 Jan 2026
Viewed by 657
Abstract
Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. [...] Read more.
Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products. Full article
(This article belongs to the Special Issue Advanced Manufacturing Processes and Technologies: Trends and Innovations)
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24 pages, 39327 KB  
Article
Forest Surveying with Robotics and AI: SLAM-Based Mapping, Terrain-Aware Navigation, and Tree Parameter Estimation
by Lorenzo Scalera, Eleonora Maset, Diego Tiozzo Fasiolo, Khalid Bourr, Simone Cottiga, Andrea De Lorenzo, Giovanni Carabin, Giorgio Alberti, Alessandro Gasparetto, Fabrizio Mazzetto and Stefano Seriani
Machines 2026, 14(1), 99; https://doi.org/10.3390/machines14010099 - 14 Jan 2026
Viewed by 623
Abstract
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation [...] Read more.
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article
(This article belongs to the Special Issue Selected Papers from MES-2025: Advances in Mechanical Engineering Solutions)
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21 pages, 12758 KB  
Article
Implementation of a Digital Twin in Additive Manufacturing of Copper—Methodology, Implications, and Future Prospects
by Moritz Benedikt Schäfle, Michel Fett, Philipp Bojunga, Florian Sondermann and Eckhard Kirchner
Machines 2026, 14(1), 97; https://doi.org/10.3390/machines14010097 - 13 Jan 2026
Viewed by 419
Abstract
Digital twins are increasingly being used to visualize, analyze, or control physical processes and systems. Implementation currently poses challenges for users due to the cross-domain complexity of digital twins. In this study, the authors utilize a self-developed method to support the implementation of [...] Read more.
Digital twins are increasingly being used to visualize, analyze, or control physical processes and systems. Implementation currently poses challenges for users due to the cross-domain complexity of digital twins. In this study, the authors utilize a self-developed method to support the implementation of a digital twin (DT) for a powder bed fusion additive manufacturing system (PBF-LB/M) for copper components, utilizing a green laser. The study highlights the support offered by the developed approach and the implications of using DTs for PBF of copper. The DT focuses in particular on monitoring maintenance requirements, assisting in the selection of correct process parameters, and alerting plant operators in the event of problems. In addition, a process model focused on lack of fusion is implemented, based on earlier studies. In the human–machine system, DTs thus represent a further building block towards an improved process stability in PBF-LB/M of copper, and the method used lowers the barrier to entry for widespread use of DTs. Full article
(This article belongs to the Section Advanced Manufacturing)
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18 pages, 3663 KB  
Article
Trajectory Tracking Control of a Six-Axis Robotic Manipulator Based on an Extended Kalman Filter-Based State Observer
by Jianxuan Liu, Tao Chen, Zhen Dou, Xiaojuan Li and Xiangjun Zou
Machines 2026, 14(1), 78; https://doi.org/10.3390/machines14010078 - 8 Jan 2026
Viewed by 466
Abstract
To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman [...] Read more.
To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman filter is combined with an extended state observer to perform the real-time observation of both internal and external disturbances in the system, accurately estimating system uncertainty and external disturbances. This approach reduces noise interference while significantly improving the correction accuracy of position and tracking errors. Second, an improved nonsingular fast terminal sliding mode controller with an optimized convergence law is introduced to ensure stability during the tracking process, effectively mitigate oscillation phenomena, and accelerate the system’s convergence speed. Finally, the convergence of the proposed method is analyzed by constructing an appropriate Lyapunov function. Simulation and experimental results strongly validate the superior performance of the proposed control strategy, demonstrating that the system can achieve high-precision trajectory tracking under the complex coupled effects of a six-axis robotic manipulator, and exhibits significant advantages in terms of accuracy and robustness. Full article
(This article belongs to the Special Issue Sensing to Cognition: The Evolution of Robotic Vision)
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25 pages, 15524 KB  
Article
A Model-Based Digital Toolbox for Unified Kinematics and Dimensional Synthesis in Parallel Robot Design
by Zhen He, Chengjin Hu, Tengfei Tang, Hanliang Fang, Yibo Jiang, Fufu Yang and Jun Zhang
Machines 2026, 14(1), 52; https://doi.org/10.3390/machines14010052 - 31 Dec 2025
Viewed by 404
Abstract
A unified digital toolbox is introduced for kinematics analysis and dimension synthesis of parallel robots, addressing challenges in configuration diversity and computational complexity. By integrating hierarchical kinematic modeling with screw theory, the toolbox establishes standardized analytical frameworks for mobility, inverse kinematics and dexterity [...] Read more.
A unified digital toolbox is introduced for kinematics analysis and dimension synthesis of parallel robots, addressing challenges in configuration diversity and computational complexity. By integrating hierarchical kinematic modeling with screw theory, the toolbox establishes standardized analytical frameworks for mobility, inverse kinematics and dexterity evaluation. A modular toolbox architecture—comprising interactive, data, external module, database and functional layers—enables systematic design, workspace estimation and dexterity-driven optimization. A hybrid MATLAB-C++ interface ensures computational efficiency and scalability. The efficacy of the toolbox is demonstrated through a case study on a novel 2UPR-2RPS parallel mechanism, achieving optimized dimensional parameters (k1 = 0.85, k2 = 1.3, k3 = 0.85, k4 = 1.3) with a mean dexterity index of 0.637 and validated workspace symmetry. Results confirm that the toolbox streamlines the design process, ensures computational accuracy and enables rapid adaptation to new robotic configurations. This work provides a robust foundation for advanced parallel robot design, offering significant potential for industrial and research applications requiring high-precision motion control. Full article
(This article belongs to the Special Issue Intelligent Design and Application of Parallel Robots)
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22 pages, 1439 KB  
Article
Memetic Algorithm for Energy Optimization in Point-to-Point Robotized Operations
by Sandi Baressi Šegota, Domagoj Frank, Ivan Lorencin and Nikola Anđelić
Machines 2026, 14(1), 35; https://doi.org/10.3390/machines14010035 - 25 Dec 2025
Viewed by 481
Abstract
This paper presents a memetic algorithm (MA) for energy cost estimation of a robot path. The developed algorithm uses a random recombination genetic algorithm (GA) as the basis for the first stage of the algorithm and performs a local search based on feature [...] Read more.
This paper presents a memetic algorithm (MA) for energy cost estimation of a robot path. The developed algorithm uses a random recombination genetic algorithm (GA) as the basis for the first stage of the algorithm and performs a local search based on feature importances determined from the data in the second stage. To allow for the faster determination of the solution quality, the algorithm uses an ML-driven fitness function, based on MLP, for the determination of path energy. The performed tests show that not only does the GA itself optimize the point-to-point paths well, but the usage of MA can lower the energy use by 58% on average (N = 100) when compared to a linear path between the same two points. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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25 pages, 7587 KB  
Article
LiMS-MFormer: A Lightweight Multi-Scale and Multi-Dimensional Attention Transformer for Robust Rolling Bearing Fault Diagnosis Under Complex Conditions
by Haixiao Cao, Chuanlong Ding, Yonghong Zhang and Liang Jiang
Machines 2026, 14(1), 32; https://doi.org/10.3390/machines14010032 - 25 Dec 2025
Cited by 2 | Viewed by 495
Abstract
Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, [...] Read more.
Bearings are critical components in industrial machinery, and their failures can lead to equipment downtime and significant safety hazards. Traditional fault diagnosis methods rely on manually crafted features and classical classifiers, often suffering from poor robustness, weak generalization under noisy or small-sample conditions, and limited suitability for lightweight deployment. This study proposes a Lightweight Multi-Scale Multi-Dimensional Self-Attention Transformer (LiMS-MFormer)—an end-to-end lightweight fault diagnosis framework integrating multi-scale feature extraction and multi-dimensional attention. The model integrates lightweight multi-scale convolutional feature extraction, hierarchical feature fusion, and a multi-dimensional self-attention mechanism to balance feature expressiveness with computational efficiency. Specifically, the front end employs Ghost convolution and enhanced residual structures for efficient multi-scale feature extraction. The middle layers perform cross-scale concatenation and fusion to enrich contextual representations. The back end introduces a lightweight temporal-channel-spatial attention module for global modeling and focuses on key patterns. Experiments on the Paderborn University (PU) dataset and the University of Ottawa bearing vibration dataset (Ottawa dataset) show that LiMS-MFormer achieves an accuracy of 96.68% on the small-sample PU dataset while maintaining minimal parameters (0.07 M) and low computational cost (13.55 M FLOPs). Moreover, under complex noisy conditions, the proposed model demonstrates strong fault diagnosis capability. On the University of Ottawa dataset, LiMS-MFormer consistently outperforms several state-of-the-art lightweight models, exhibiting superior accuracy, robustness, and generalization in challenging diagnostic tasks. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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37 pages, 7149 KB  
Article
An AI Digital Platform for Fault Diagnosis and RUL Estimation in Drivetrain Systems Under Varying Operating Conditions
by Dimitrios M. Bourdalos, Xenofon D. Konstantinou, Josef Koutsoupakis, Ilias A. Iliopoulos, Kyriakos Kritikakos, George Karyofyllas, Panayotis E. Spiliotopoulos, Ioannis E. Saramantas, John S. Sakellariou, Dimitrios Giagopoulos, Spilios D. Fassois, Panagiotis Seventekidis and Sotirios Natsiavas
Machines 2026, 14(1), 26; https://doi.org/10.3390/machines14010026 - 24 Dec 2025
Viewed by 842
Abstract
Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using [...] Read more.
Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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49 pages, 13896 KB  
Review
A Review on In-Situ Monitoring in Wire Arc Additive Manufacturing: Technologies, Applications, Challenges, and Needs
by Mohammad Arjomandi, Jackson Motley, Quang Ngo, Yoosuf Anees, Muhammad Ayaan Afzal and Tuhin Mukherjee
Machines 2026, 14(1), 19; https://doi.org/10.3390/machines14010019 - 22 Dec 2025
Cited by 2 | Viewed by 1724
Abstract
Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate [...] Read more.
Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation. Full article
(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)
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19 pages, 3914 KB  
Article
Analysis and Experiment of Damping Characteristics of Multi-Hole Pressure Pulsation Attenuator
by Shenghao Zhou, Na Zhou, Yukang Zhang, Guoshuai Wang, Xinyu Li, Hui Ma and Junzhe Lin
Machines 2026, 14(1), 11; https://doi.org/10.3390/machines14010011 - 19 Dec 2025
Viewed by 371
Abstract
Aviation hydraulic systems operate under high pressure and large flow rates, which induce significant fluid pressure pulsations and hydraulic shocks in pipelines. These pulsations, exacerbated by complex external loads, can lead to excessive vibration stress, component damage, oil leakage, and compromised system safety. [...] Read more.
Aviation hydraulic systems operate under high pressure and large flow rates, which induce significant fluid pressure pulsations and hydraulic shocks in pipelines. These pulsations, exacerbated by complex external loads, can lead to excessive vibration stress, component damage, oil leakage, and compromised system safety. While existing methods—such as pump structure optimization, pipeline layout adjustment, and active control—can reduce pulsations to some extent, they are limited by cost, reliability, and adaptability, particularly under high-pressure and multi-excitation conditions. Passive control, using pressure pulsation damping devices, has proven to be more practical; however, conventional designs typically focus on low-load systems and have limited frequency adaptability. This paper proposes a multi-hole parallel pressure pulsation damping device that offers high vibration attenuation, broad adaptability, and easy installation. A combined simulation–experiment approach is employed to investigate its damping mechanism and performance. The results indicate that the damping device effectively reduces vibrations in the 200–500 Hz range, with minimal impact from changes in load pressure and rotational speed. Under a high pressure of 21 MPa and a speed of 1500 rpm, the maximum insertion loss can reach 15.82 dB, significantly reducing the pressure pulsation in the hydraulic pipeline. Full article
(This article belongs to the Section Machine Design and Theory)
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23 pages, 14105 KB  
Article
A Comprehensive Study on Meshing Performances Compensation for Face-Hobbed Hypoid Gears: Coupled Analysis of Spatial Installation Errors and Manufactured Tooth Flank Characteristics
by Chengcheng Liang, Yihao Zhang, Longhua Liu, Chaosheng Song and Siyuan Liu
Machines 2025, 13(12), 1145; https://doi.org/10.3390/machines13121145 - 16 Dec 2025
Viewed by 296
Abstract
In manufacturing face-hobbing hypoid gears, the coupling between tooth flank errors and installation errors has a significant impact on dynamic meshing behavior, yet quantitative models for their synergistic effects remain scarce. This study elucidates the combined effects of three-dimensional (3D) installation errors and [...] Read more.
In manufacturing face-hobbing hypoid gears, the coupling between tooth flank errors and installation errors has a significant impact on dynamic meshing behavior, yet quantitative models for their synergistic effects remain scarce. This study elucidates the combined effects of three-dimensional (3D) installation errors and real tooth flank deviations on transmission error. First, a geometric model of the real tooth flank, incorporating midpoint pitch deviation, is established based on theoretical flank equations and coordinate transformations. Then, a finite element model integrating 3D installation errors is developed. Finally, the combined effects of installation errors and real tooth flanks on meshing performance are analyzed. Results reveal a dual role of installation errors: when compensating for midpoint pitch deviation, the peak-to-peak transmission error (PPTE) decreases by 3.78%, while the contact pattern length and area increase. Under certain conditions, despite a 26.28% increase in PPTE, the contact pattern length grows by 2.29%, accompanied by a notable reduction in maximum contact stress on the tooth flanks. Full article
(This article belongs to the Section Advanced Manufacturing)
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51 pages, 2572 KB  
Review
Digital Twin Approaches for Gear NVH Optimization: A Literature Review of Modeling, Data Integration, and Validation Gaps
by Krisztian Horvath and Ambrus Zelei
Machines 2025, 13(12), 1141; https://doi.org/10.3390/machines13121141 - 15 Dec 2025
Viewed by 777
Abstract
Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating [...] Read more.
Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article
(This article belongs to the Special Issue Digital Twin-Driven Machine Performance and Reliability: Replication, Prediction and Front Running Simulation)
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25 pages, 2873 KB  
Article
Dynamic Attention Analysis of Body Parts in Transformer-Based Human–Robot Imitation Learning with the Embodiment Gap
by Yoshiki Tsunekawa and Kosuke Sekiyama
Machines 2025, 13(12), 1133; https://doi.org/10.3390/machines13121133 - 10 Dec 2025
Cited by 1 | Viewed by 1001
Abstract
In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic [...] Read more.
In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic attention to body parts in imitation learning between humans and robots based on a Transformer model. To adapt human imitation movements to a robot, we solved forward and inverse kinematics using the Levenberg–Marquardt method and performed feature extraction using the k-means method to make the data suitable for Transformer input. The imitation learning process is carried out using the Transformer. UMAP is employed to visualize the attention layer within the Transformer. As a result, this system enabled imitation of movements while focusing on multiple body parts between humans and robots with an embodiment gap, revealing the transitions of body parts receiving attention and their relationships in the robot’s acquired imitation movements. Full article
(This article belongs to the Special Issue Robots with Intelligence: Developments and Applications)
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19 pages, 2938 KB  
Article
Adaptive Funnel Control of Hydraulic Excavator Based on Neural Network
by Yuhe Li and Xiaowen Qi
Machines 2025, 13(12), 1132; https://doi.org/10.3390/machines13121132 - 9 Dec 2025
Viewed by 513
Abstract
To address the challenge of controlling the hydraulic excavator’s precise motion, a nonlinear backstepping control algorithm is designed, combining a funnel function and a neural network (NN), which effectively compensates for the influence of unmodeled dynamics and external disturbances on the hydraulic excavator’s [...] Read more.
To address the challenge of controlling the hydraulic excavator’s precise motion, a nonlinear backstepping control algorithm is designed, combining a funnel function and a neural network (NN), which effectively compensates for the influence of unmodeled dynamics and external disturbances on the hydraulic excavator’s control system. Specifically, an improved funnel function is introduced to characterize both the steady-state and transient performance of the system simultaneously, thereby limiting the joint tracking error within predetermined performance constraints and enhancing the trajectory tracking accuracy. Two RBFNN estimators are employed to address the uncertain coupled mechanical dynamics and nonlinear hydraulic dynamics, respectively. The weight updating law is generated based on the gradient descent method, which can prevent high-gain feedback and enhance the system’s robustness. Finally, the stability of the closed-loop system is rigorously proven using the Lyapunov function analysis method. To verify the effectiveness of the proposed algorithm, simulations and experimental research are conducted under various external disturbances, using the excavator’s flat working condition as a case study. The results demonstrate that the controller maintains good control performance and robustness even in the presence of uncertainties and external disturbances within the system. Full article
(This article belongs to the Section Automation and Control Systems)
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25 pages, 10110 KB  
Article
Gear Fault Classification and Diagnosis Based on Gear Transmission Errors: Theoretical and Experimental Research
by Siliang Wang, Naige Wang, Anil Kumar and Jianlong Wang
Machines 2025, 13(12), 1093; https://doi.org/10.3390/machines13121093 - 26 Nov 2025
Viewed by 692
Abstract
Among gearbox faults, gear tooth faults are dominant. Although the traditional vibration spectrum analysis method is the mainstream diagnostic method, it has limitations such as sensitivity to environmental noise and high sensor deployment cost. Based on the influence of the meshing stiffness of [...] Read more.
Among gearbox faults, gear tooth faults are dominant. Although the traditional vibration spectrum analysis method is the mainstream diagnostic method, it has limitations such as sensitivity to environmental noise and high sensor deployment cost. Based on the influence of the meshing stiffness of the faulty gear on the dynamic transmission error of the gear, this study innovatively proposes to use the transmission error to diagnose and identify typical gear tooth faults. This paper first calculates the time-varying stiffness of typical faulty gear teeth based on the potential energy method, and analyzes the influence of various faults and environmental noise on the dynamic transmission error signal and vibration signal by establishing a six-degree-of-freedom gear transmission dynamics model. Then, a gear transmission experimental platform is built to synchronously collect the vibration acceleration and transmission error data of the gearbox. The convolutional neural network is used to classify the data under different sample lengths and different noise intensities. The results show that the transmission error signal under the same conditions has a higher gear fault diagnosis accuracy. The proposed method can not only improve the accuracy and anti-interference of gear fault diagnosis but also reduce the deployment cost of signal acquisition, providing a new paradigm for gear condition monitoring. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)
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27 pages, 1160 KB  
Article
Integrated Production–Logistics Scheduling in Flexible Assembly Shops Using an Improved Genetic Algorithm
by Jie Fu, Bin Yang, Zhixing Chang, Yuanrong Zhang, Jiarui Wang, Xiaotong Wang and Lei Wang
Machines 2025, 13(12), 1090; https://doi.org/10.3390/machines13121090 - 26 Nov 2025
Cited by 1 | Viewed by 725
Abstract
Achieving high operational efficiency in modern manufacturing requires the seamless integration of production scheduling and intralogistics coordination. However, in flexible assembly shops, the decoupling between production sequencing and automated guided vehicle (AGV) routing often leads to resource conflicts, unbalanced workloads, and inefficient energy [...] Read more.
Achieving high operational efficiency in modern manufacturing requires the seamless integration of production scheduling and intralogistics coordination. However, in flexible assembly shops, the decoupling between production sequencing and automated guided vehicle (AGV) routing often leads to resource conflicts, unbalanced workloads, and inefficient energy utilization. To address this challenge, this study proposes an improved genetic algorithm (IGA) for integrated production–logistics scheduling. The innovation lies in a triple-chain encoding strategy that concurrently represents production, transportation, and time-window constraints, coupled with adaptive crossover and mutation operators for enhanced population diversity. Furthermore, a time-window-constrained Dijkstra routing mechanism is incorporated to prevent AGV conflicts and improve synchronization between machines and logistics. Two representative shop-floor scenarios—baseline and disturbed conditions—were designed for validation. Comparative experiments against a standard genetic algorithm (GA) and a two-stage heuristic demonstrate that the IGA achieves 9.5% and 6.7% reductions in average makespan, respectively, while maintaining less than 1% deviation under 10% random disturbances. Statistical tests (p < 0.01, Cohen’s d > 1.4) confirm the method’s robustness and practical effectiveness. The proposed approach provides a reliable and implementable optimization framework that enhances coordination between production and AGV systems in flexible assembly environments and offers a practical reference for smart manufacturing scheduling and digital twin applications. Full article
(This article belongs to the Section Advanced Manufacturing)
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18 pages, 9591 KB  
Article
Elastic-Snapping–Driven Butterfly Stroke: A Soft Robotic Fish
by Lin Tian, Ruo-Pu Chen, Yu Zhao, Zhi-Peng Wang, Jiao Jia, Weifeng Yuan, Xi-Qiao Feng and Zi-Long Zhao
Machines 2025, 13(12), 1078; https://doi.org/10.3390/machines13121078 - 24 Nov 2025
Viewed by 711
Abstract
The locomotion of fish provides inspiration for designing efficient and agile underwater robots. Potamotrygon motoro propels itself by generating traveling waves along its pectoral fins. Inspired by its graceful swimming stroke, we design and fabricate a robotic fish, where the snap-through instability of [...] Read more.
The locomotion of fish provides inspiration for designing efficient and agile underwater robots. Potamotrygon motoro propels itself by generating traveling waves along its pectoral fins. Inspired by its graceful swimming stroke, we design and fabricate a robotic fish, where the snap-through instability of elastic curved rods is exploited to produce the undulatory fin motion. In this design, the rotary input of two motors is transformed smoothly and continuously to controllable wave-like fin deformation. By changing the initial fin shape, motor speed, and friction at the releasing end, the propulsion performance and the maneuverability of the robotic fish can be significantly improved. The physical prototype of the robotic fish is fabricated, and its swimming performance is measured. Its maximum swimming speed reaches 0.76 BL/s, and it can achieve small-radius turns with a maximum angular speed of 1.25 rad/s. In contrast to the multi-actuator systems, the proposed dual-motor, elastic-snapping–driven design is featured by simple structural construction, low energy consumption, excellent maneuverability, and superb adaptation to environments. Our robotic fish holds promising applications in such areas as environmental monitoring, underwater inspection, and ocean exploration. The propulsion strategy presented in this work may pave a new way for the design of shape-morphing robots as well as other soft machines at multiple length scales. Full article
(This article belongs to the Special Issue Innovations in Soft Robotics: Enhancing Safety, Performance, and Dexterity)
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44 pages, 8496 KB  
Article
Identification and Evaluation of Vibration Sources from Experiments on Laboratory Drilling Equipment
by Patrik Flegner, Ján Kačur, Gabriel Wittenberger, Milan Durdán and Marek Laciak
Machines 2025, 13(12), 1076; https://doi.org/10.3390/machines13121076 - 21 Nov 2025
Cited by 2 | Viewed by 853
Abstract
Rotary rock drilling generates vibration signals that capture the dynamic behavior of the drilling system, the interaction between the tool and the rock, and the progression of tool wear. These signals, traditionally considered undesirable, have become a key source of information for condition [...] Read more.
Rotary rock drilling generates vibration signals that capture the dynamic behavior of the drilling system, the interaction between the tool and the rock, and the progression of tool wear. These signals, traditionally considered undesirable, have become a key source of information for condition monitoring and predictive maintenance. This study experimentally investigates vibration sources and diagnostic indicators using a laboratory horizontal drilling stand equipped with accelerometers and controlled operating regimes. Six regimes were evaluated, ranging from idle operation of individual aggregates (motor, pump, hydrogenerator) to drilling of concrete and granite under defined process parameters. Vibration data were analyzed in the time, frequency, and time–frequency domains using RMS, variance, spectral centroid, spectral entropy, FFT-based spectra, autocorrelation, cross-correlation, and spectrograms. The results confirm the research hypothesis that selected vibration-based indicators correlate with tool degradation. Increased RMS values, higher variance, reduced correlation symmetry, and a shift of spectral energy above 6 kHz reliably reflect wear progression and changes in the dynamic response of the system. Spectrograms further reveal transient phases and redistribution of vibration energy during drilling. The findings demonstrate that vibration measurements enable the identification and separation of vibration sources related to aggregates and processes. The extracted diagnostic features form a basis for intelligent monitoring and predictive algorithms in rotary drilling, supporting advanced condition monitoring strategies within Industry 4.0. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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26 pages, 4060 KB  
Review
A Research Review of Rolling Bearing Turbocharger Modeling and System Characteristics
by Zhiheng Yu, Zhiyong Zhang, Jinrui Pu, Qi Xue, Yuanhao Li and Tianyou Wang
Machines 2025, 13(11), 1066; https://doi.org/10.3390/machines13111066 - 19 Nov 2025
Viewed by 1246
Abstract
In recent years, due to the growing imbalance between energy consumption and available resources, as well as strict CO2 emission regulations, turbochargers have become increasingly important in applications such as automobiles, ships, and aerospace. Turbochargers can effectively increase the intake volume of [...] Read more.
In recent years, due to the growing imbalance between energy consumption and available resources, as well as strict CO2 emission regulations, turbochargers have become increasingly important in applications such as automobiles, ships, and aerospace. Turbochargers can effectively increase the intake volume of engine cylinders, improving fuel combustion efficiency and engine power. In order to meet the growing demand for more energy-efficient, lower-carbon-emission systems, it is necessary to design more compact, efficient, durable, and affordable supercharging systems. Compared with traditional floating ring bearings, rolling bearing turbochargers have become a greater focus of research due to their excellent transient performance, low friction loss, and strong load-bearing capacity. Due to the large number of components, complex structure, lightweight high-load rotor, complicated operating conditions, and unclear nonlinear vibration mechanism of rolling bearing turbochargers, it is necessary to establish a refined model to clarify how factors such as bearing and squeeze film damper parameters and rotor operating parameters affect the system response. Therefore, this study reviews relevant research in this field from the perspectives of modeling and system characteristics and points out directions for future research. Full article
(This article belongs to the Section Turbomachinery)
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19 pages, 2801 KB  
Article
Research on Denoising Methods for Infrasound Leakage Signals Using Improved Wavelet Threshold Algorithm
by Zunmin Liu, Jingchun Tang, Baogang Li, Yuhuan Li and Fazhan Yang
Machines 2025, 13(11), 1062; https://doi.org/10.3390/machines13111062 - 18 Nov 2025
Viewed by 576
Abstract
Infrasound leakage signals, with low propagation energy loss, are ideal for long-distance and small leakage detection but suffer severe background noise interference. Existing wavelet denoising methods using traditional soft/hard threshold functions face critical limitations: soft thresholds introduce constant deviation, while hard thresholds cause [...] Read more.
Infrasound leakage signals, with low propagation energy loss, are ideal for long-distance and small leakage detection but suffer severe background noise interference. Existing wavelet denoising methods using traditional soft/hard threshold functions face critical limitations: soft thresholds introduce constant deviation, while hard thresholds cause discontinuities, both leading to suboptimal noise reduction for infrasound signals—this gap hinders accurate leakage detection. To address this, we propose a wavelet denoising method with an improved threshold function, analyze its process via the Mallat algorithm, and prove its continuity and convergence. Comparative experiments on infrasound leakage data show that, at the optimal decomposition level, our method reduces RMSE by 41.19% and increases SNR by 5.1326 dB compared to the soft threshold method; versus the hard threshold method, RMSE decreases by 34.65% and SNR increases by 4.2148 dB. It also separates background noise more thoroughly in time–frequency domains, demonstrating strong feasibility for pipeline infrasound leakage detection. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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36 pages, 12016 KB  
Article
Federated Learning-Enabled Secure Multi-Modal Anomaly Detection for Wire Arc Additive Manufacturing
by Mohammad Mahruf Mahdi, Md Abdul Goni Raju, Kyung-Chang Lee and Duck Bong Kim
Machines 2025, 13(11), 1063; https://doi.org/10.3390/machines13111063 - 18 Nov 2025
Cited by 1 | Viewed by 1319
Abstract
This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor [...] Read more.
This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article
(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)
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18 pages, 10019 KB  
Article
Belt Sanding Robot for Large Convex Surfaces Featuring SEA Arms and an Active Re-Tensioner with PI Force Control
by Hongjoo Jin, Chanhyuk Moon, Taegyun Kim and TaeWon Seo
Machines 2025, 13(11), 1012; https://doi.org/10.3390/machines13111012 - 2 Nov 2025
Viewed by 791
Abstract
This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active [...] Read more.
This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active tension regulation, which limits coverage and makes force distribution difficult to control. The mechanism consists of two series elastic actuator arms and an active re-tensioner that adjusts belt tension during contact. In contrast to a conventional belt sander, the series elastic configuration enables indirect estimation of the reaction force without load cells and provides compliant interaction with contact transients. The system is evaluated on curved steel plates using vertical scans with a belt width of 50 mm and a drive wheel speed of 300 rpm. Performance is reported for two target curvature values, namely 0.47 and 1.37, with five trials for each condition. The control objective is a constant normal force along the contact, achieved through proportional–integral control of the arms for normal-force tracking and the re-tensioner for belt tension regulation. To quantify spatial force uniformity, the distribution rate is defined as the ratio of the difference between the maximum and minimum normal forces to the maximum normal force measured across the belt–workpiece contact region. Compared with a simple belt sander baseline, the proposed system increased the sanded area coverage by 31.85%, from 62.20% to 94.05%, at the curvature value of 0.47, and by 8.49%, from 81.21% to 89.70%, at the curvature value of 1.37. The distribution rate improved by 113% at the curvature value of 0.47 and by 16.7% at the curvature value of 1.37. Under identical operating conditions of 50 mm belt width, 300 rpm, and five repeated trials, these results indicate higher area coverage and more uniform force distribution relative to the baseline. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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11 pages, 2368 KB  
Article
Experimental Evaluation of a Line-Start Consequent-Pole Surface Permanent-Magnet Motor with Simple Rotor Design Strategies for Performance Improvement
by Yuichi Yokoi, Yasuhiro Miyamoto and Tsuyoshi Higuchi
Machines 2025, 13(11), 1003; https://doi.org/10.3390/machines13111003 - 31 Oct 2025
Viewed by 648
Abstract
The line-start permanent-magnet (LSPM) motor combines the direct-on-line starting of induction motors with the high efficiency of permanent-magnet (PM) synchronous motors, but conventional interior PM designs are difficult to manufacture and surface PM (SPM) designs often suffer from limited starting torque and reduced [...] Read more.
The line-start permanent-magnet (LSPM) motor combines the direct-on-line starting of induction motors with the high efficiency of permanent-magnet (PM) synchronous motors, but conventional interior PM designs are difficult to manufacture and surface PM (SPM) designs often suffer from limited starting torque and reduced efficiency. This paper investigates consequent-pole SPM designs, in which the number of magnets is reduced by half while maintaining equal magnet volume, enabling simple rotor construction and improved starting performance. A prototype is manufactured and tested, confirming smooth synchronization under load. Efficiency is constrained by the non-sinusoidal flux distribution of the consequent-pole structure. Rotor design strategies enlarging the air gap near the iron poles are analyzed, and a finite element method analysis shows improved torque and efficiency without loss of starting capability. Full article
(This article belongs to the Section Electrical Machines and Drives)
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21 pages, 5218 KB  
Article
Biomimetic Nonlinear X-Shaped Vibration Isolation System for Jacket Offshore Platforms
by Zhenghan Zhu and Yangmin Li
Machines 2025, 13(11), 998; https://doi.org/10.3390/machines13110998 - 30 Oct 2025
Cited by 1 | Viewed by 802
Abstract
Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. [...] Read more.
Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article
(This article belongs to the Special Issue Vibration Isolation and Control in Mechanical Systems)
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24 pages, 9133 KB  
Article
Compound Fault Diagnosis of Hydraulic Pump Based on Underdetermined Blind Source Separation
by Xiang Wu, Pengfei Xu, Shanshan Song, Shuqing Zhang and Jianyu Wang
Machines 2025, 13(10), 971; https://doi.org/10.3390/machines13100971 - 21 Oct 2025
Viewed by 720
Abstract
The difficulty in precisely extracting single-fault signatures from hydraulic pump composite faults, which stems from structural complexity and coupled multi-source vibrations, is tackled herein via a new diagnostic technique based on underdetermined blind source separation (UBSS). Utilizing sparse component analysis (SCA), the proposed [...] Read more.
The difficulty in precisely extracting single-fault signatures from hydraulic pump composite faults, which stems from structural complexity and coupled multi-source vibrations, is tackled herein via a new diagnostic technique based on underdetermined blind source separation (UBSS). Utilizing sparse component analysis (SCA), the proposed method achieves blind source separation without relying on prior knowledge or multiple sensors. However, conventional SCA-based approaches are limited by their reliance on a predefined number of sources and their high sensitivity to noise. To overcome these limitations, an adaptive source number estimation strategy is proposed by integrating information–theoretic criteria into density peak clustering (DPC), enabling automatic source number determination with negligible additional computation. To facilitate this process, the short-time Fourier transform (STFT) is first employed to convert the vibration signals into the frequency domain. The resulting time–frequency points are then clustered using the integrated DPC–Bayesian Information Criterion (BIC) scheme, which jointly estimates both the number of sources and the mixing matrix. Finally, the original source signals are reconstructed through the minimum L1-norm optimization method. Simulation and experimental studies, including hydraulic pump composite fault experiments, verify that the proposed method can accurately separate mixed vibration signals and identify distinct fault components even under low signal-to-noise ratio (SNR) conditions. The results demonstrate the method’s superior separation accuracy, noise robustness, and adaptability compared with existing algorithms. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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18 pages, 5002 KB  
Article
Wear Analysis of Conical Picks with Different Self-Rotatory Speeds
by Youhang Zhou, Xin Peng, Zhuxi Ma and Fang Li
Machines 2025, 13(10), 957; https://doi.org/10.3390/machines13100957 - 17 Oct 2025
Viewed by 860
Abstract
The conical pick is an essential component of roadheaders used for cutting rock. During the rock-breaking process, these picks interact with the rock, resulting in self-rotation, which enhances the wear uniformity of conical picks, thereby prolonging their service life. Since the phenomenon of [...] Read more.
The conical pick is an essential component of roadheaders used for cutting rock. During the rock-breaking process, these picks interact with the rock, resulting in self-rotation, which enhances the wear uniformity of conical picks, thereby prolonging their service life. Since the phenomenon of self-rotation is generated passively by random contact forces with the rock surface, it is challenging to quantitatively measure the extent of self-rotatory speed. In order to investigate the correlation between the self-rotatory speed of conical picks and wear, this article establishes various self-rotatory speeds for vertical rock-breaking wear experiments involving conical picks. It analyzes the relationship between quantitative parameters, such as the equivalent stress and wear, through simulation. The results of the study indicate that the optimal self-rotatory speed of the conical pick is 16 rpm when it is rotated vertically to break the rock, resulting in minimal wear. When the equivalent stress and Mohr–Coulomb safety factor are optimized, it is essential to consider the changes in normal force and the variation in the area affected by the safety factor. This leads to an increase in wear as the cutting distance increases, indicating that a higher self-rotatory speed does not necessarily improve the wear performance of conical picks. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 1847 KB  
Article
A Novel Two-Stage Gas-Excitation Sampling and Sample Delivery Device: Simulation and Experiments
by Xu Yang, Dewei Tang, Qiquan Quan and Zongquan Deng
Machines 2025, 13(10), 958; https://doi.org/10.3390/machines13100958 - 17 Oct 2025
Viewed by 649
Abstract
Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the [...] Read more.
Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the potential origins of life. To enable efficient sample acquisition under vacuum and microgravity conditions, this study introduces a two-stage gas-driven asteroid sampling strategy. This approach mitigates the challenges posed by low-gravity environments and irregular asteroid topography. A coupled computational fluid dynamics–discrete element method (CFD–DEM) framework was employed to simulate the gas–solid two-phase flow during the sampling process. First, a model of the first-stage gas-driven sampling device was developed to establish the relationship between the inlet angle of the gas nozzle and the sampling efficiency, leading to the optimization of the nozzle’s structural parameters. Subsequently, a model of the integrated two-stage gas-driven sampling and sample-delivery system was constructed, through which the influence of the second-stage nozzle inlet angle on the total collected sample mass was investigated, and its design parameters were further refined. Simulation outcomes were validated against experimental data, confirming the reliability of the CFD–DEM coupling approach for predicting gas–solid two-phase interactions. The results demonstrate the feasibility of collecting asteroid regolith with the proposed two-stage gas-driven sampling and delivery system, thereby providing a practical pathway for extraterrestrial material acquisition. Full article
(This article belongs to the Section Machine Design and Theory)
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18 pages, 15086 KB  
Article
Design of a PM-Assisted Synchronous Reluctance Motor with Enhanced Performance and Lower Cost for Household Appliances
by Yuli Bao and Chenyang Xia
Machines 2025, 13(10), 954; https://doi.org/10.3390/machines13100954 - 16 Oct 2025
Viewed by 1723
Abstract
Conventional permanent magnet-assisted synchronous reluctance machine (PMaSynRM) suffers from limited power factor and efficiency. To boost these, the use of sintered rare earth permanent magnets (PMs) is an option, with respect to sintered ferrite, resulting in a high-performance PMaSynRM (HP-PMaSynRM). However, the increasing [...] Read more.
Conventional permanent magnet-assisted synchronous reluctance machine (PMaSynRM) suffers from limited power factor and efficiency. To boost these, the use of sintered rare earth permanent magnets (PMs) is an option, with respect to sintered ferrite, resulting in a high-performance PMaSynRM (HP-PMaSynRM). However, the increasing price of rare earth PM can lead to an overall increase in machine cost. To overcome this issue, a novel HP-PMaSynRM is presented in this paper. Structurally, the proposed four-pole HP-PMaSynRM rotor is characterized by two fluid-shaped flux barriers filled with sintered ferrite, as well as a cut-off region. Based on the finite element analysis (FEA) results, the proposed HP-PMaSynRM exhibits higher performance compared with the conventional HP-PMaSynRM with rare earth PMs. It is shown that the proposed HP-PMaSynRM has higher power factor, efficiency, and better torque quality over a wide range of operating conditions. Moreover, the HP-PMaSynRM presented incurs lower cost. Finally, the proposed HP-PMaSynRM is manufactured, tested, and compared with the conventional benchmark HP-PMaSynRM, proving its advantages, including higher power factor, higher efficiency, lower torque oscillation, and lower cost. Full article
(This article belongs to the Special Issue New Advances in Synchronous Reluctance Motors)
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26 pages, 10016 KB  
Article
Robot Path Planning Based on Improved PRM for Wing-Box Internal Assembly
by Jiefeng Jiang, Yong You, Youtao Shao, Yunbo Bi and Jingjing You
Machines 2025, 13(10), 952; https://doi.org/10.3390/machines13100952 - 16 Oct 2025
Viewed by 928
Abstract
Currently, fastener installation within the narrow, confined space of a wing box must be performed manually, as existing robotic systems are unable to adequately meet the internal assembly requirements. To address this problem, a new robot with one prismatic and five revolute joints [...] Read more.
Currently, fastener installation within the narrow, confined space of a wing box must be performed manually, as existing robotic systems are unable to adequately meet the internal assembly requirements. To address this problem, a new robot with one prismatic and five revolute joints (1P5R) has been developed for entering and operating inside the wing box. Firstly, the mechanical structure and control system of the robot were designed and implemented. Then, an improved Probabilistic Roadmap (PRM) method was developed to enable rapid and smooth path planning, mainly depending on optimization of sampling strategy based on Halton sequence, an elliptical-region-based redundant point optimization strategy using control points, improving roadmap construction, and path smoothing based on B-spline curves. Finally, obstacle–avoidance path planning based on the improved PRM was simulated using the MoveIt platform, corresponding robotic motion experiments were conducted, and the improved PRM was validated. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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42 pages, 8498 KB  
Article
Encoding Multivariate Time Series of Gas Turbine Data as Images to Improve Fault Detection Reliability
by Enzo Losi, Mauro Venturini, Lucrezia Manservigi and Giovanni Bechini
Machines 2025, 13(10), 943; https://doi.org/10.3390/machines13100943 - 13 Oct 2025
Viewed by 923
Abstract
The monitoring and diagnostics of energy equipment aim to detect anomalies in time series data in order to support predictive maintenance and avoid unplanned shutdowns. Thus, the paper proposes a novel methodology that utilizes sequence-to-image transformation methods to feed Convolutional Neural Networks (CNNs) [...] Read more.
The monitoring and diagnostics of energy equipment aim to detect anomalies in time series data in order to support predictive maintenance and avoid unplanned shutdowns. Thus, the paper proposes a novel methodology that utilizes sequence-to-image transformation methods to feed Convolutional Neural Networks (CNNs) for diagnostic purposes. Multivariate time series taken from real gas turbines are transformed by using two methods. We study two CNN architectures, i.e., VGG-19 and SqueezeNet. The investigated anomaly is the spike fault. Spikes are implanted in field multivariate time series taken during normal operation of ten gas turbines and composed of twenty gas path measurements. Six fault scenarios are simulated. For each scenario, different combinations of fault parameters are considered. The main novel contribution of this study is the development of a comprehensive framework, which starts from time series transformation and ends up with a diagnostic response. The potential of CNNs for image recognition is applied to the gas path field measurements of a gas turbine. A hard-to-detect type of fault (i.e., random spikes of different magnitudes and frequencies of occurrence) was implanted in a seemingly real-world fashion. Since spike detection is highly challenging, the proposed framework has both scientific and industrial relevance. The extended and thorough analyses unequivocally prove that CNNs fed with images are remarkably more accurate than TCN models fed with raw time series data, with values higher than 93% if the number of implanted spikes is 10% of the total data and a gain in accuracy of up to 40% in the most realistic scenario. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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19 pages, 7767 KB  
Article
Fabric Flattening with Dual-Arm Manipulator via Hybrid Imitation and Reinforcement Learning
by Youchun Ma, Fuyuki Tokuda, Akira Seino, Akinari Kobayashi, Mitsuhiro Hayashibe and Kazuhiro Kosuge
Machines 2025, 13(10), 923; https://doi.org/10.3390/machines13100923 - 6 Oct 2025
Viewed by 1074
Abstract
Fabric flattening is a critical pre-processing step for automated garment manufacturing. Most existing approaches employ single-arm robotic systems that act at a single contact point. Due to the nonlinear and deformable dynamics of fabric, such systems often require multiple actions to achieve a [...] Read more.
Fabric flattening is a critical pre-processing step for automated garment manufacturing. Most existing approaches employ single-arm robotic systems that act at a single contact point. Due to the nonlinear and deformable dynamics of fabric, such systems often require multiple actions to achieve a fully flattened state. This study introduces a dual-arm fabric-flattening method based on a cascaded Proposal–Action network with a hybrid training framework. The PA network is first trained through imitation learning from human demonstrations and is subsequently refined through reinforcement learning with real-world flattening feedback. Experimental results demonstrate that the hybrid training framework substantially improves the overall flattening success rate compared with a policy trained only on human demonstrations. The success rate for a single flattening operation increases from 74% to 94%, while the overall success rate improves from 82% to 100% after two rounds of training. Furthermore, the learned policy, trained exclusively on baseline fabric, generalizes effectively to fabrics with varying thicknesses and stiffnesses. The approach reduces the number of required flattening actions while maintaining a high success rate, thereby enhancing both efficiency and practicality in automated garment manufacturing. Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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23 pages, 1883 KB  
Review
Multisensor Data Fusion-Driven Digital Twins in Computer Numerical Control Machining: A Review
by Yang Cao
Machines 2025, 13(10), 921; https://doi.org/10.3390/machines13100921 - 6 Oct 2025
Cited by 2 | Viewed by 1697
Abstract
As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product [...] Read more.
As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development. Full article
(This article belongs to the Special Issue Smart Tools in Advanced Machining)
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24 pages, 14760 KB  
Article
Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion
by Fang Yang, En Dong, Zhidan Zhong, Weiqi Zhang, Yunhao Cui and Jun Ye
Machines 2025, 13(10), 914; https://doi.org/10.3390/machines13100914 - 3 Oct 2025
Cited by 1 | Viewed by 1171
Abstract
Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, [...] Read more.
Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article
(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)
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20 pages, 1951 KB  
Article
Virtual Prototyping of the Human–Robot Ecosystem for Multiphysics Simulation of Upper Limb Motion Assistance
by Rocco Adduci, Francesca Alvaro, Michele Perrelli and Domenico Mundo
Machines 2025, 13(10), 895; https://doi.org/10.3390/machines13100895 - 1 Oct 2025
Cited by 1 | Viewed by 737
Abstract
As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily [...] Read more.
As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily affordable. Moreover, devices are not easily accepted by patients, who can refuse to use them due to not feeling comfortable. The presented work proposes the exploitation of a virtual prototype of the human–robot ecosystem for the study and analysis of patient–robot interactions, enabling their simulation-based investigation in multiple scenarios. For the accomplishment of this task, the Dynamics of Multi-physical Systems platform, previously presented by the authors, is further developed to enable the integration of biomechanical models of the human body with mechatronics models of robotic devices for motion assistance, as well as with PID-based control strategies. The work begins with (1) a description of the background; hence, the current state of the art and purpose of the study; (2) the platform is then presented and the system is formalized, first from a general side and then (3) in the application-specific scenario. (4) The use case is described, presenting a controlled gym weightlifting exercise supported by an exoskeleton and the results are analyzed in a final paragraph (5). Full article
(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)
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18 pages, 2718 KB  
Article
Metamodel-Based Digital Twin Architecture with ROS Integration for Heterogeneous Model Unification in Robot Shaping Processes
by Qingxin Li, Peng Zeng, Qiankun Wu and Hualiang Zhang
Machines 2025, 13(10), 898; https://doi.org/10.3390/machines13100898 - 1 Oct 2025
Cited by 2 | Viewed by 3523
Abstract
Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these [...] Read more.
Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article
(This article belongs to the Section Advanced Manufacturing)
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15 pages, 5285 KB  
Article
A Multi-Layer Triboelectric Material Deep Groove Ball Bearing Triboelectric Nanogenerator: Speed and Skidding Monitoring
by Zibao Zhou, Long Wang, Zihao Wang and Fengtao Wang
Machines 2025, 13(9), 875; https://doi.org/10.3390/machines13090875 - 19 Sep 2025
Viewed by 1112
Abstract
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip [...] Read more.
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip state of the rolling elements. The DGTG utilizes a copper inner ring charge supplementation mechanism to maintain the maximum charge density on the rolling element, thereby ensuring a strong electrical signal output. The deviation between the output frequency of the electrical signal and the theoretical value allows for effective monitoring of the slip state during bearing operation. Experimental results demonstrate that when the inner ring speed ranges from 100 to 2000 rpm, the open-circuit voltage generally remains above 30 V. The short-circuit current signal exhibits a fitting coefficient of R2 = 0.99997 with respect to the roller’s rotational speed frequency and motor speed, while the open-circuit voltage signal shows a fitting coefficient of R2 = 0.99984, indicating a strong linear relationship and a good response to varying speeds. Compared to the traditional photoelectric sensors commonly used in industry, the measurement difference between the three signals is consistently less than 5.5%, and real-time monitoring of the slip rate is possible when compared to the theoretical value. The DGTG developed in this study occupies minimal space, offers high reliability, and fully leverages the bearing structure, enabling real-time monitoring of bearing speed and slip. Full article
(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)
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24 pages, 8964 KB  
Article
Dynamic Siting and Coordinated Routing for UAV Inspection via Hierarchical Reinforcement Learning
by Qingyun Yang, Yewei Zhang and Shuyi Shao
Machines 2025, 13(9), 861; https://doi.org/10.3390/machines13090861 - 17 Sep 2025
Cited by 2 | Viewed by 1319
Abstract
To enhance the efficiency and reduce the operational costs of large-scale Unmanned Aerial Vehicle (UAV) inspection missions limited by endurance, this paper addresses the coupled problem of dynamically positioning landing/takeoff sites and routing the UAVs. A novel Hierarchical Reinforcement Learning (H-DRL) framework is [...] Read more.
To enhance the efficiency and reduce the operational costs of large-scale Unmanned Aerial Vehicle (UAV) inspection missions limited by endurance, this paper addresses the coupled problem of dynamically positioning landing/takeoff sites and routing the UAVs. A novel Hierarchical Reinforcement Learning (H-DRL) framework is proposed, which decouples the problem into a high-level strategic deployment policy and a low-level tactical routing policy. The primary contribution of this work lies in two architectural innovations that enable globally coordinated, end-to-end optimization. First, a coordinated credit assignment mechanism is introduced, where the high-level policy communicates its strategic guidance to the low-level policy via a learned “intent vector,” facilitating intelligent collaboration. Second, an Energy-Aware Graph Attention Network (Ea-GAT) is designed for the low-level policy. By endogenously embedding an energy feasibility model into its attention mechanism, the Ea-GAT guarantees the generation of dynamically feasible flight paths. Comprehensive simulations and a physical experiment validate the proposed framework. The results demonstrate a significant improvement in mission efficiency, with the makespan reduced by up to 16.3%. This work highlights the substantial benefits of joint optimization for dynamic robotic applications. Full article
(This article belongs to the Section Automation and Control Systems)
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20 pages, 2785 KB  
Article
Dynamic Posture Programming for Robotic Milling Based on Cutting Force Directional Stiffness Performance
by Yuhang Gao, Tianyang Qiu, Ci Song, Senjie Ma, Zhibing Liu, Zhiqiang Liang and Xibin Wang
Machines 2025, 13(9), 822; https://doi.org/10.3390/machines13090822 - 6 Sep 2025
Cited by 1 | Viewed by 1213
Abstract
Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path [...] Read more.
Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article
(This article belongs to the Section Advanced Manufacturing)
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35 pages, 6930 KB  
Article
A Slip-Based Model Predictive Control Approach for Trajectory Following of Unmanned Tracked Vehicles
by Ismail Gocer and Selahattin Caglar Baslamisli
Machines 2025, 13(9), 817; https://doi.org/10.3390/machines13090817 - 5 Sep 2025
Cited by 2 | Viewed by 1201
Abstract
In the field of tracked vehicle dynamics, studies show that vertical loads are concentrated under road wheels on firm road conditions, allowing slip-based models of tracked vehicles to be designed similar to wheeled vehicle models. This paper proposes a slip-based nonlinear two-track prediction [...] Read more.
In the field of tracked vehicle dynamics, studies show that vertical loads are concentrated under road wheels on firm road conditions, allowing slip-based models of tracked vehicles to be designed similar to wheeled vehicle models. This paper proposes a slip-based nonlinear two-track prediction model for model predictive control (MPC), where track forces under road wheels are calculated with a simplification procedure implemented onto shear displacement theory. The study includes a comparative analysis with a kinematic prediction model, examining scenarios such as constant speed cornering and spiral maneuvers. Validation is carried out by comparing the simulation results of the proposed controller with field test data acquired from a five-wheeled tracked vehicle platform, including measurements on asphalt and stabilized road conditions. The results demonstrate that the slip-based model excels in trajectory tracking, with lateral deviations consistently below 0.25 m and typically around 0.02–0.08 m RMS depending on the scenario. By improving the computational efficiency and ensuring precise navigation, this approach offers an advanced control solution for tracked vehicles on firm terrain. Full article
(This article belongs to the Special Issue Optimization-Based Motion Planning & Control for Autonomous Driving in Dynamic Environments)
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20 pages, 1325 KB  
Article
Intelligent Fault Diagnosis for Cross-Domain Few-Shot Learning of Rotating Equipment Based on Mixup Data Augmentation
by Kun Yu, Yan Li, Qiran Zhan, Yongchao Zhang and Bin Xing
Machines 2025, 13(9), 807; https://doi.org/10.3390/machines13090807 - 3 Sep 2025
Cited by 2 | Viewed by 1748
Abstract
Existing fault diagnosis methods assume the identical distribution of training and test data, failing to adapt to source–target domain differences in industrial scenarios and limiting generalization. They also struggle to explore inter-domain correlations with scarce labeled target samples, leading to poor convergence and [...] Read more.
Existing fault diagnosis methods assume the identical distribution of training and test data, failing to adapt to source–target domain differences in industrial scenarios and limiting generalization. They also struggle to explore inter-domain correlations with scarce labeled target samples, leading to poor convergence and generalization. To address this, our paper proposes a cross-domain few-shot intelligent fault diagnosis method based on Mixup data augmentation. Firstly, a Mixup data augmentation method is used to linearly combine source domain and target domain data in a specific proportion to generate mixed-domain data, enabling the model to learn correlations and features between data from different domains and improving its generalization ability in cross-domain few-shot learning tasks. Secondly, a feature decoupling module based on the self-attention mechanism is proposed to extract domain-independent features and domain-related features, allowing the model to further reduce the domain distribution gap and effectively generalize source domain knowledge to the target domain. Then, the model parameters are optimized through a multi-task learning mechanism consisting of sample classification tasks and domain classification tasks. Finally, applications in classification tasks on multiple sets of equipment fault datasets show that the proposed method can significantly improve the fault recognition ability of the diagnosis model under the conditions of large distribution differences in the target domain and scarce labeled samples. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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36 pages, 9288 KB  
Article
Robotic Contact on Complex Curved Surfaces Using Adaptive Trajectory Planning Through Precise Force Control
by Hosham Wahballa, Abubker Ahmed, Ghazally I. Y. Mustafa, Mohammednour Gibreel and Lei Weining
Machines 2025, 13(9), 794; https://doi.org/10.3390/machines13090794 - 2 Sep 2025
Cited by 1 | Viewed by 1858
Abstract
This paper presents a control method for achieving precise robotic contact on complex and curved surfaces in manufacturing and automation. The method combines smooth trajectory planning with contact force control to improve finishing accuracy while reducing processing time. It integrates a Bézier curve [...] Read more.
This paper presents a control method for achieving precise robotic contact on complex and curved surfaces in manufacturing and automation. The method combines smooth trajectory planning with contact force control to improve finishing accuracy while reducing processing time. It integrates a Bézier curve with a simplified hexic polynomial implemented through a position-based impedance controller that is enhanced by a novel force corrector unit. The model is referred to as the Adaptive Bézier–Based Impedance Constant Force Controller (ABBIFC), where the Bézier curve length is calculated using Simpson’s rule, and surface orientations are interpolated using quadratic quaternions. A hexic polynomial velocity profile ensures consistent motion speed throughout the process. This method effectively regulates both contact force and positional accuracy, resulting in high-quality surface finishes. Simulation studies and real-time polishing experiments demonstrate the system’s capability to accurately track path, speed, and force, with significantly reduced force errors. This approach advances robotic automation in applications such as polishing, grinding, and other surface finishing tasks by ensuring smooth motion and precise force control. Full article
(This article belongs to the Special Issue Advances and Challenges in Robotic Manipulation)
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24 pages, 3681 KB  
Article
A Novel Transfer Kernel Enabled Kernel Extreme Learning Machine Model for Cross-Domain Condition Monitoring and Fault Diagnosis of Bearings
by Haobo Yang, Hui Wang, Jing Meng, Wenhui Sun and Chao Chen
Machines 2025, 13(9), 793; https://doi.org/10.3390/machines13090793 - 1 Sep 2025
Viewed by 794
Abstract
Kernel transfer learning (KTL), as a kind of statistical transfer learning (STL), has provided significant solutions for cross-domain condition monitoring and fault diagnosis of bearings due to its ability to capture relationships and reduce the gap between source and target domains. However, most [...] Read more.
Kernel transfer learning (KTL), as a kind of statistical transfer learning (STL), has provided significant solutions for cross-domain condition monitoring and fault diagnosis of bearings due to its ability to capture relationships and reduce the gap between source and target domains. However, most conventional kernel transfer methods only set a weighting parameter ranging from 0 to 1 for those functions measuring cross-domain differences, while the intra-domain differences are ignored, which fails to completely characterize the distributional differences to some extent. To overcome these challenges, a novel transfer kernel enabled kernel extreme learning machine (TK-KELM) model is proposed. For model pre-training, a parallel structure is designed to represent the state and change of vibration signals more comprehensively. Subsequently, intra-domain correlation is introduced into the kernel function, which aims to release the weight parameters that describe the inter-domain correlation and break the range limit of 0–1. Consequently, intra-domain as well as inter-domain correlations can boost the authenticity of the transfer kernel jointly. Furthermore, a similarity-guided feature-directed transfer kernel optimization strategy (SFTKOS) is proposed to refine model parameters by calculating domain similarity across different feature scales. Subsequently, the kernels extracted from different scales are fused as the core functions of TK-KELM. In addition, an integration framework via function principal component analysis with maximum mean difference (FPCA-MMD) is designed to extract the multi-scale domain-invariant degradation indicator for boosting the performance of TK-KELM. Finally, related experiments verify the effectiveness and superiority of the proposed TK-KELM model, improving the accuracy of condition monitoring and fault diagnosis. Full article
(This article belongs to the Special Issue Advanced Signal Processing Methods and Deep Neural Networks for Machine Fault Diagnosis)
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22 pages, 3918 KB  
Article
Evaluating Mental Workload and Productivity in Manufacturing: A Neuroergonomic Study of Human–Robot Collaboration Scenarios
by Carlo Caiazzo, Marko Djapan, Marija Savkovic, Djordje Milojevic, Arso Vukicevic and Luca Gualtieri
Machines 2025, 13(9), 783; https://doi.org/10.3390/machines13090783 - 1 Sep 2025
Cited by 3 | Viewed by 2238
Abstract
The field of human–robot collaboration (HRC) still lacks research studies regarding the evaluation of mental workload (MWL) through objective measurement to assess the mental state of operators in assembly tasks. This research study presents a comparative neuroergonomic analysis to evaluate the mental workload [...] Read more.
The field of human–robot collaboration (HRC) still lacks research studies regarding the evaluation of mental workload (MWL) through objective measurement to assess the mental state of operators in assembly tasks. This research study presents a comparative neuroergonomic analysis to evaluate the mental workload and productivity in three laboratory experimental conditions: in the first, the participant assembles a component without the intervention of the robot (standard scenario); in the second scenario, the participant performs the same activity in collaboration with the robot (collaborative scenario); in the third scenario, the participant is fully guided in the task in collaboration with the robot (collaborative guided scenario) through a system of guiding labels according to Poka-Yoke principles. The assessment of participants’ mental workload is shown through combinative analysis of subjective (NASA TLX) and objective (electroencephalogram—EEG). Objective MWL was assessed as the power waves ratio β/α (Beta—stress indicator, Alpha—relaxation indicator). Furthermore, the research used observational measurements to calculate the productivity index in terms of accurately assembled components across the three scenarios. Through ANOVA RM, mental workload significantly decreased in the activities involving the cobot. Also, an increase in productivity was observed shifting from the manual scenario to the cobot-assisted one (18.4%), and to the collaborative guided scenarios supported by Poka-Yoke principles (33.87%). Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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20 pages, 9282 KB  
Article
Electromagnetic Vibration Characteristics Analysis of Large-Scale Doubly Fed Induction Machines Under Multiple Operating Conditions
by Haoyu Kang, Yiming Ma, Liyang Liu, Fanqi Huang and Libing Zhou
Machines 2025, 13(9), 777; https://doi.org/10.3390/machines13090777 - 30 Aug 2025
Viewed by 884
Abstract
The electromagnetic vibration characteristics of doubly fed induction machines (DFIMs) employed in variable-speed pumped storage units, which must accommodate frequent power response and operational mode transitions, serve as critical indicators for assessing unit safety and stability. Nevertheless, there persists a significant research gap [...] Read more.
The electromagnetic vibration characteristics of doubly fed induction machines (DFIMs) employed in variable-speed pumped storage units, which must accommodate frequent power response and operational mode transitions, serve as critical indicators for assessing unit safety and stability. Nevertheless, there persists a significant research gap regarding generalized vibration analysis models and comprehensive investigations into their steady-state and dynamic vibration performance. To address this challenge, this study develops a universal analytical model for electromagnetic excitation forces in DFIMs using Maxwell’s stress tensor method, explicitly incorporating operational conditions such as rotor eccentricity and load imbalance. Using a 300 MW DFIM as a case study, we employ a hybrid numerical-analytical approach to examine the detrimental effects of harmonic currents generated by rotor-side converters. Furthermore, we systematically analyze how spatial harmonics induced by mechanical faults and temporal harmonics arising from electrical faults collectively influence the electromagnetic vibration behavior. Experimental validation conducted on a 10 MW DFIM prototype through vibration displacement measurements confirms the efficacy of the proposed analytical framework. Full article
(This article belongs to the Special Issue Recent Advances in Energy Conversion Apparatus for Physical Energy Storage Systems: Design, Drive, and Integration)
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26 pages, 9257 KB  
Article
Synthesis of Mechanisms Based on Optimal Solution Density
by Sean Mather and Arthur Erdman
Machines 2025, 13(9), 773; https://doi.org/10.3390/machines13090773 - 28 Aug 2025
Viewed by 1000
Abstract
The traditional process for kinematic synthesis of planar mechanisms involves setting a few prescribed positions, then solving a set of equations to identify a vector chain that exactly reproduces those positions. In evaluating these equations, designers often must sift through multiple “infinities” of [...] Read more.
The traditional process for kinematic synthesis of planar mechanisms involves setting a few prescribed positions, then solving a set of equations to identify a vector chain that exactly reproduces those positions. In evaluating these equations, designers often must sift through multiple “infinities” of solutions corresponding to some number of free-choice variables that each have an infinite number of possible values. In this vast solution space, some combination of those variables will produce the most optimal solution, but finding that optimal solution is not trivial. There are two extremes for addressing the impossibility of sifting through infinite possible values. First, one could use analytical techniques to make educated estimates of the optimal values. Or, alternatively, a designer could completely remove their perspective from the process, passing the problem into a computer and programming it to sift through millions (or orders of magnitude more) possible solutions. The present work proposes a novel intermediate step in the analytical synthesis process that functions as a middle ground between these extremes. Optimizing solution density involves a designer manually manipulating the problem definition to increase the percentage of solutions that have pivots in acceptable locations. This is accomplished by changing the values of δj and αj (prescribed translation and rotation of the moving plane, respectively) to manipulate the position of the poles. A physical example, designing a 7-bar parallel-motion generator, shows that applying this method yields more passing solutions when comparing over the same search depth. Specifically, 0.008% of solutions pass the design criteria without applying the method, and 3.154% pass after optimizing. This approach can reduce the computational load placed on a computer running a search script, as designers can use larger increments on the free choices without skipping over a family of solutions. Full article
(This article belongs to the Special Issue Compliant Mechanisms and Actuators for Exoskeletons and Human–Robot Interactions)
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23 pages, 3047 KB  
Article
Trajectory Tracking Control for Wheeled Mobile Robots with Unknown Slip Rates Based on Improved Rapid Variable Exponential Reaching Law and Sliding Mode Observer
by Zexu Li, Jun Guo, Taiyuan Wang, Xiufang Xiong, Yong Feng and Xingshu Li
Machines 2025, 13(9), 765; https://doi.org/10.3390/machines13090765 - 27 Aug 2025
Cited by 1 | Viewed by 1620
Abstract
Aiming at the trajectory tracking control problem of wheeled mobile robots under unknown slip ratio conditions, this paper designs a trajectory tracking controller based on an improved rapid variable power reaching law and a sliding mode observer. First, a kinematic model of the [...] Read more.
Aiming at the trajectory tracking control problem of wheeled mobile robots under unknown slip ratio conditions, this paper designs a trajectory tracking controller based on an improved rapid variable power reaching law and a sliding mode observer. First, a kinematic model of the wheeled mobile robot is established, explicitly considering the influence of slip ratio. Then, a sliding mode observer is developed for online estimation of the slip ratio, addressing the difficulty of direct slip ratio measurement. On this basis, a trajectory tracking controller is designed based on the improved rapid variable power reaching law, enabling fast tracking of multiple complex trajectories under slip conditions. Simulation and experimental results show that the proposed trajectory tracking controller not only effectively eliminates the influence of unknown slip disturbances on trajectory tracking, improving smoothness and tracking accuracy but also greatly accelerates the convergence process. The shortest convergence time is only 20.56% of that achieved by a fuzzy PID trajectory tracking controller and 61.43% of that achieved by a rapid double power reaching law trajectory tracking controller with a sliding mode observer. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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