Machines

11 pages, 2368 KB

Open AccessEditor’s ChoiceArticle

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 379

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

Open AccessEditor’s ChoiceArticle

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

Viewed by 431

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

Open AccessEditor’s ChoiceArticle

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 370

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

Open AccessEditor’s ChoiceArticle

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 428

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

Open AccessEditor’s ChoiceArticle

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 403

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

Open AccessEditor’s ChoiceArticle

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 686

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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42 pages, 8498 KB

Open AccessEditor’s ChoiceArticle

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 574

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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23 pages, 1883 KB

Open AccessEditor’s ChoiceReview

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

Viewed by 945

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

Open AccessEditor’s ChoiceArticle

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 675

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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18 pages, 2718 KB

Open AccessEditor’s ChoiceArticle

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

Viewed by 3137

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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20 pages, 1951 KB

Open AccessEditor’s ChoiceArticle

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

Viewed by 462

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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15 pages, 5285 KB

Open AccessEditor’s ChoiceArticle

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 801

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

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 861

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

Open AccessEditor’s ChoiceArticle

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 731

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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20 pages, 1325 KB

Open AccessEditor’s ChoiceArticle

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

Viewed by 1065

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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24 pages, 3681 KB

Open AccessEditor’s ChoiceArticle

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 530

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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26 pages, 9257 KB

Open AccessEditor’s ChoiceArticle

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 719

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 δ_j and α_j (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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30 pages, 4753 KB

Open AccessEditor’s ChoiceReview

Review on Melt Electrowriting Modelling and Applications

by Hongli Ju, Wajira Mirihanage, Weiguang Wang and Zekai Murat Kilic

Machines 2025, 13(9), 763; https://doi.org/10.3390/machines13090763 - 25 Aug 2025

Viewed by 1757

Abstract

Melt electrowriting (MEW) is an advanced additive manufacturing technology that can produce micro- or nano-scale fibres, achieving accurate fibre deposition, and is suitable for manufacturing high-precision, miniature products. This review introduces the key principles and parameters that influence the performance of melt electrowriting [...] Read more.

Melt electrowriting (MEW) is an advanced additive manufacturing technology that can produce micro- or nano-scale fibres, achieving accurate fibre deposition, and is suitable for manufacturing high-precision, miniature products. This review introduces the key principles and parameters that influence the performance of melt electrowriting and explores the current mathematical modelling under four stages: (1) heating and extrusion system, (2) formation of the Taylor cone, (3) formation and injection of the melt jet, and (4) deposition of the melt jet. In addition, current applications of melt electrowriting in emerging areas, such as tissue engineering, energy, filtration, and bioengineering, are introduced while discussing its combination with other additive manufacturing technologies. Finally, recent challenges, including production time, cost, and precision are covered, while the future research directions are to improve technology and introduce new materials. Full article

(This article belongs to the Section Advanced Manufacturing)

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33 pages, 17334 KB

Open AccessEditor’s ChoiceReview

Scheduling in Remanufacturing Systems: A Bibliometric and Systematic Review

by Yufan Zheng, Wenkang Zhang, Runjing Wang and Rafiq Ahmad

Machines 2025, 13(9), 762; https://doi.org/10.3390/machines13090762 - 25 Aug 2025

Viewed by 1437

Abstract

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage [...] Read more.

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

(This article belongs to the Special Issue Intelligent Remanufacturing: Innovations, Technologies, and Sustainable Practices)

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23 pages, 5912 KB

Open AccessEditor’s ChoiceArticle

Multi-Objective Optimization Method for Comprehensive Modification of High-Contact-Ratio Asymmetrical Planetary Gear Based on Hybrid Surrogate Model

by Yuansheng Zhou, Zhongwei Tang, Bingquan Lu, Jinyuan Tang and Shaoxue Wei

Machines 2025, 13(9), 757; https://doi.org/10.3390/machines13090757 - 24 Aug 2025

Viewed by 739

Abstract

The contact performance of high-contact-ratio asymmetrical planetary gears is comprehensively evaluated using multiple indicators. The relationship between the indicators and modification parameters is difficult to accurately describe with a single type of surrogate model due to their varying degrees of nonlinearity. This paper [...] Read more.

The contact performance of high-contact-ratio asymmetrical planetary gears is comprehensively evaluated using multiple indicators. The relationship between the indicators and modification parameters is difficult to accurately describe with a single type of surrogate model due to their varying degrees of nonlinearity. This paper proposes an optimization design method for comprehensive modification parameters based on a hybrid surrogate model to improve the optimization accuracy of comprehensive modification. Firstly, a theoretical model of comprehensive modification for high-contact-ratio asymmetrical planetary gears and a dynamically selected hybrid surrogate model are proposed based on different contact performance indicators. Then, the explicit constraints of comprehensive modification and the implicit constraints of non-edge contact are modeled for the modification parameters. Finally, a multi-objective optimization algorithm for the modification parameters based on the hybrid surrogate model is established and validated through experiments and simulations. The results show that the proposed method improves the optimization accuracy and edge contact on the tooth surface is avoided while improving the contact performance, and they provide a reference for efficient and precise optimization of high-contact-ratio asymmetrical planetary gears. Full article

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

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20 pages, 5937 KB

Open AccessEditor’s ChoiceArticle

Stator Fault Diagnostics in Asymmetrical Six-Phase Induction Motor Drives with Model Predictive Control Applicable During Transient Speeds

by Hugo R. P. Antunes, Davide. S. B. Fonseca, João Serra and Antonio J. Marques Cardoso

Machines 2025, 13(8), 740; https://doi.org/10.3390/machines13080740 - 19 Aug 2025

Viewed by 671

Abstract

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection [...] Read more.

Abrupt speed variations and motor start-ups have been pointed out as critical challenges in the framework of fault diagnostics in induction motor drives, namely inter-turn short circuit faults. Generally, abrupt accelerations influence the typical symptoms of the fault, and consequently, the fault detection becomes ambiguous, impacting prompt and effective decision-making. To overcome this issue, this study proposes an inter-turn short-circuit fault diagnostic technique for asymmetrical six-phase induction motor drives operating under both smooth and abrupt motor accelerations. A time–frequency domain spectrogram of the AC component extracted from the q-axis reference current signal serves as a reliable fault indicator. This technique stands out for the compromise between robustness and computational effort using only one control variable accessible in the model predictive control algorithm, thus discarding both voltage and current signals. Experimental tests involving various load torques and fault severities, in transient regimes, were performed to validate the proposed methodology’s effectiveness thoroughly. Full article

(This article belongs to the Section Electrical Machines and Drives)

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18 pages, 6922 KB

Open AccessEditor’s ChoiceArticle

Compact Liquid Cooling Garment with Integrated Vapor Compression Refrigeration for Extreme High-Temperature Environments

by Yuancheng Zhu, Yonghong He and Weiguo Xiong

Machines 2025, 13(8), 738; https://doi.org/10.3390/machines13080738 - 19 Aug 2025

Viewed by 1009

Abstract

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This [...] Read more.

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This system innovatively integrates a patented evaporator-pump module and an optimized miniature rotary compressor, achieving a 151 W cooling capacity at 55 °C ambient temperature, surpassing existing portable systems in compactness and performance. Human trials with eight male participants at 35 °C (walking) and 40 °C (sitting) demonstrated that the liquid cooling garment system significantly improved thermal comfort. The mean thermal comfort vote decreased from 2.63 (uncomfortable) to 1.13 (slightly uncomfortable) while walking and from 3.88 (very uncomfortable) to 1.25 (slightly uncomfortable) while sitting. The mean skin temperature in the final stable state was reduced by 0.34 °C in walking trials and 1.09 °C in sitting trials, and heart rate decreased by up to 10.2 bpm in sedentary conditions. Comprehensive human trials under extreme heat further validate this system’s efficacy. This lightweight, efficient system offers a practical solution for personal thermal management in extreme high-temperature environments, with potential applications in industrial safety, military operations, and emergency response. Full article

(This article belongs to the Section Turbomachinery)

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16 pages, 1470 KB

Open AccessEditor’s ChoiceArticle

Experimental Analysis of a Coaxial Magnetic Gear Prototype

by Stefano Lovato, Giovanni Barosco, Ludovico Ortombina, Riccardo Torchio, Piergiorgio Alotto, Maurizio Repetto and Matteo Massaro

Machines 2025, 13(8), 716; https://doi.org/10.3390/machines13080716 - 12 Aug 2025

Viewed by 735

Abstract

Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed [...] Read more.

Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed at the Department of Industrial Engineering of the University of Padova. It is found that its efficiency is high and aligned with prototypes in the literature, its stationary response confirms the velocity ratio of the corresponding mechanical planetary gear, the overload protection is aligned with numerical prediction, while the dynamic response highlights that the intrinsic compliance of the magnetic coupling prevents the use of such device in high-frequency transients. It is concluded that the proposed architecture can be effectively employed for speed reducers applications where low-frequency modulation is sufficient, which includes many industrial applications. Nevertheless, high rotational speeds are allowed. The performance characteristics, although specific for the prototype considered, experimentally highlights the key features of coaxial magnetic gear devices. The experimental performance are also compared with estimations from the literature, when available. Full article

(This article belongs to the Special Issue Dynamics and Lubrication of Gears)

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21 pages, 7202 KB

Open AccessEditor’s ChoiceArticle

Monocular Vision-Based Swarm Robot Localization Using Equilateral Triangular Formations

by Taewon Kang, Ji-Wook Kwon, Il Bae and Jin Hyo Kim

Machines 2025, 13(8), 667; https://doi.org/10.3390/machines13080667 - 29 Jul 2025

Viewed by 914

Abstract

Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system [...] Read more.

Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system is designed to operate in fully open spaces, without landmarks or support from positioning infrastructures. To achieve this, we propose a localization method based on equilateral triangular formations. By leveraging the geometric properties of equilateral triangles, the accurate two-dimensional position of each participating robot is estimated using one-dimensional lateral distance information between robots, which can be reliably and accurately obtained with a low-cost monocular vision sensor. Experimental and simulation results demonstrate that, as travel time increases, the positioning error of the proposed method becomes significantly smaller than that of a conventional dead-reckoning system, another low-cost localization approach applicable to open environments. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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20 pages, 3716 KB

Open AccessEditor’s ChoiceArticle

Modeling and Validation of a Spring-Coupled Two-Pendulum System Under Large Free Nonlinear Oscillations

by Borislav Ganev, Marin B. Marinov, Ivan Kralov and Anastas Ivanov

Machines 2025, 13(8), 660; https://doi.org/10.3390/machines13080660 - 28 Jul 2025

Viewed by 1245

Abstract

Studying nonlinear oscillations in mechanical systems is fundamental to understanding complex dynamic behavior in engineering applications. While classical analytical methods remain valuable for systems with limited complexity, they become increasingly inadequate when nonlinearities are strong and geometrically induced, as in the case of [...] Read more.

Studying nonlinear oscillations in mechanical systems is fundamental to understanding complex dynamic behavior in engineering applications. While classical analytical methods remain valuable for systems with limited complexity, they become increasingly inadequate when nonlinearities are strong and geometrically induced, as in the case of large-amplitude oscillations. This paper presents a combined numerical and experimental investigation of a mechanical system composed of two coupled pendulums, exhibiting significant nonlinear behavior due to elastic deformation throughout their motion. A mathematical model of the system was developed using the MatLab/Simulink ver.6.1 environment, considering gravitational, inertial, and nonlinear elastic restoring forces. One of the major challenges in accurately modeling such systems is accurately representing damping, particularly in the absence of dedicated dampers. In this work, damping coefficients were experimentally identified through decrement measurements and incorporated into the simulation model to improve predictive accuracy. The simulation outputs, including angular displacements, velocities, accelerations, and phase trajectories over time, were validated against experimental results obtained via high-precision inertial sensors. The comparison shows a strong correlation between numerical and experimental data, with minimal relative errors in amplitude and frequency. This research represents the first stage of a broader study aimed at analyzing forced and parametrically excited oscillations. Beyond validating the model, the study contributes to the design of a robust experimental framework suitable for further exploration of nonlinear dynamics. The findings have practical implications for the development and control of mechanical systems subject to dynamic loads, with potential applications in automation, vibration analysis, and system diagnostics. Full article

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

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22 pages, 6221 KB

Open AccessEditor’s ChoiceArticle

Development and Experimental Validation of a Tubular Permanent Magnet Linear Alternator for Free-Piston Engine Applications

by Parviz Famouri, Jayaram Subramanian, Fereshteh Mahmudzadeh-Ghomi, Mehar Bade, Terence Musho and Nigel Clark

Machines 2025, 13(8), 651; https://doi.org/10.3390/machines13080651 - 25 Jul 2025

Viewed by 1227

Abstract

The ongoing rise in global electricity demand highlights the need for advanced, efficient, and environmentally responsible energy conversion technologies. This research presents a comprehensive design, modeling, and experimental validation of a tubular permanent magnet linear alternator (PMLA) integrated with a free piston engine [...] Read more.

The ongoing rise in global electricity demand highlights the need for advanced, efficient, and environmentally responsible energy conversion technologies. This research presents a comprehensive design, modeling, and experimental validation of a tubular permanent magnet linear alternator (PMLA) integrated with a free piston engine system. Linear alternators offer a direct conversion of linear motion to electricity, eliminating the complexity and losses associated with rotary generators and enabling higher efficiency and simplified system architecture. The study combines analytical modeling, finite element simulations, and a sensitivity-based design optimization to guide alternator and engine integration. Two prototype systems, designated as alpha and beta, were developed, modeled, and tested. The beta prototype achieved a maximum electrical output of 550 W at 57% efficiency using natural gas fuel, demonstrating reliable performance at elevated reciprocating frequencies. The design and optimization of specialized flexure springs were essential in achieving stable, high-frequency operation and improved power density. These results validate the effectiveness of the proposed design approach and highlight the scalability and adaptability of PMLA technology for sustainable power generation. Ultimately, this study demonstrates the potential of free piston linear generator systems as efficient, robust, and environmentally friendly alternatives to traditional rotary generators, with applications spanning hybrid electric vehicles, distributed energy systems, and combined heat and power. Full article

(This article belongs to the Section Electrical Machines and Drives)

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22 pages, 6181 KB

Open AccessEditor’s ChoiceArticle

Speed Sensorless Control for a Six-Phase Induction Machine Based on a Sliding Mode Observer

by Larizza Delorme, Magno Ayala, Osvaldo Gonzalez, Jorge Rodas, Raúl Gregor and Jesus C. Hernandez

Machines 2025, 13(8), 639; https://doi.org/10.3390/machines13080639 - 23 Jul 2025

Cited by 2 | Viewed by 973

Abstract

This paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness [...] Read more.

This paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness and accuracy of the developed sensorless scheme are verified by experimental results, which demonstrate the system’s performance under various operating conditions. These results demonstrate the advantages of the proposal as a valid alternative to the conventional method, which uses a mechanical speed sensor for multiphase machines. Additionally, the sensorless approach can also serve as a redundant backup in the event of mechanical sensor failure, thereby increasing the reliability of the overall drive system. Full article

(This article belongs to the Special Issue Recent Progress in Electrical Machines and Motor Drives)

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27 pages, 11254 KB

Open AccessEditor’s ChoiceArticle

Improved RRT-Based Obstacle-Avoidance Path Planning for Dual-Arm Robots in Complex Environments

by Jing Wang, Genliang Xiong, Bowen Dang, Jianli Chen, Jixian Zhang and Hui Xie

Machines 2025, 13(7), 621; https://doi.org/10.3390/machines13070621 - 18 Jul 2025

Viewed by 1717

Abstract

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a [...] Read more.

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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30 pages, 4492 KB

Open AccessEditor’s ChoiceArticle

Hard Preloaded Duplex Ball Bearing Dynamic Model for Space Applications

by Pablo Riera, Luis Maria Macareno, Igor Fernandez de Bustos and Josu Aguirrebeitia

Machines 2025, 13(7), 581; https://doi.org/10.3390/machines13070581 - 4 Jul 2025

Cited by 1 | Viewed by 758

Abstract

Duplex ball bearings are common components in space satellite mechanisms, and their behaviour impacts the overall performance and reliability of these systems. During rocket launches, these bearings suffer high vibrational loads, making their dynamic response essential for their survival. To predict the dynamic [...] Read more.

Duplex ball bearings are common components in space satellite mechanisms, and their behaviour impacts the overall performance and reliability of these systems. During rocket launches, these bearings suffer high vibrational loads, making their dynamic response essential for their survival. To predict the dynamic behaviour under vibration, simulations and experimental tests are performed. However, published models for space applications fail to capture the variations observed in test responses. This study presents a multi-degree-of-freedom nonlinear multibody model of a hard-preloaded duplex space ball bearing, particularized for this work to the case in which the outer ring is attached to a shaker and the inner ring to a test dummy mass. The model incorporates the Hunt and Crossley contact damping formulation and employs quaternions to accurately represent rotational dynamics. The simulated model response is validated against previously published axial test data, and its response under step, sine, and random excitations is analysed both in the case of radial and axial excitation. The results reveal key insights into frequency evolution, stress distribution, gapping phenomena, and response amplification, providing a deeper understanding of the dynamic performance of space-grade ball bearings. Full article

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

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21 pages, 13574 KB

Open AccessEditor’s ChoiceArticle

Ultra-Local Model-Based Adaptive Enhanced Model-Free Control for PMSM Speed Regulation

by Chunlei Hua, Difen Shi, Xi Chen and Guangfa Gao

Machines 2025, 13(7), 541; https://doi.org/10.3390/machines13070541 - 21 Jun 2025

Viewed by 651

Abstract

Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC) [...] Read more.

Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC) scheme based on an ultra-local model for permanent magnet synchronous motor (PMSM) drives. First, by integrating a nonlinear disturbance observer (NDOB) and a PD control law into the generalized model-free controller, an enhanced model-free speed controller (EMFSC) was developed to ensure closed-loop stability. Compared with a conventional MFSC, the proposed method eliminated steady-state errors, reduced the speed overshoot, and achieved faster settling with improved disturbance rejection. Second, to address the performance degradation induced by input gain

α

mismatch during time-varying load conditions, we developed an online parameter identification method for real-time

α

estimation. This adaptive mechanism enabled automatic controller parameter adjustment, which significantly enhanced the transient tracking performance of the PMSM drive. Furthermore, an algebraic-framework-based high-precision identification technique is proposed to optimize the initial

α

selection, which effectively reduces the parameter tuning effort. Simulation and experimental results demonstrated that the proposed AEMFSC significantly enhanced the PMSM’s robustness against load torque variations and parameter uncertainties. Full article

(This article belongs to the Topic Advanced Electrical Machines and Drives Technologies, 2nd Edition)

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35 pages, 707 KB

Open AccessEditor’s ChoiceSystematic Review

Security by Design for Industrial Control Systems from a Cyber–Physical System Perspective: A Systematic Mapping Study

by Ahmed Elmarkez, Soraya Mesli-Kesraoui, Pascal Berruet and Flavio Oquendo

Machines 2025, 13(7), 538; https://doi.org/10.3390/machines13070538 - 20 Jun 2025

Cited by 1 | Viewed by 2096

Abstract

Industrial Control Systems (ICSs), a specialized type of Cyber–Physical System, have shifted from isolated and obscured environments to ones exposed to diverse Information Technology (IT) security threats, which are now highly interconnected. Their adoption of IT introduces vulnerabilities which they were not originally [...] Read more.

Industrial Control Systems (ICSs), a specialized type of Cyber–Physical System, have shifted from isolated and obscured environments to ones exposed to diverse Information Technology (IT) security threats, which are now highly interconnected. Their adoption of IT introduces vulnerabilities which they were not originally designed to handle, posing critical risks. Thus, it’s imperative to integrate security measures early in CPS development, particularly during the design and implementation phases, to mitigate these vulnerabilities effectively. This study aims to identify, classify, and analyze existing research on the security-by-design paradigm for CPSs, exploring trends and defining the characteristics, advantages, limitations, and open issues of current methodologies. A systematic mapping study was conducted, selecting 55 primary studies through a rigorous protocol. The findings indicate that the majority of methodologies concentrate on the design phase, frequently overlooking other stages of development. Moreover, while there is a notable emphasis on security analysis across most primary studies, there is a notable gap in considering the integration of mitigation measures. This oversight raises concerns about the efficacy of security measures in real-world deployment scenarios. Additionally, there is a significant reliance on human intervention, highlighting the need for further development in automated security solutions. Conflicts between security requirements and other system needs are also inadequately addressed, potentially compromising overall system effectiveness. This work provides a comprehensive overview of CPS security-by-design methodologies and identifies several open issues that require further investigation, emphasizing the need for a holistic approach that includes vulnerability handling, clear security objectives, and effective conflict management, along with improved standard integration, advanced validation methods, and automated tools. Full article

(This article belongs to the Special Issue Emerging Approaches to Intelligent and Autonomous Systems)

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23 pages, 5700 KB

Open AccessEditor’s ChoiceArticle

Near-Zero Parasitic Shift Rectilinear Flexure Stages Based on Coupled n-RRR Planar Parallel Mechanisms

by Loïc Tissot-Daguette, Célestin Vallat, Marijn Nijenhuis, Florent Cosandier and Simon Henein

Machines 2025, 13(6), 530; https://doi.org/10.3390/machines13060530 - 18 Jun 2025

Cited by 2 | Viewed by 975

Abstract

Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel [...] Read more.

Flexure-based linear stages have become prevalent in precision engineering; however, most designs suffer from parasitic shifts that degrade positioning accuracy. Conventional solutions to mitigate these parasitic motions often compromise support stiffness, reduce motion range, and increase structural complexity. This study presents a novel family of flexure-based rectilinear-motion stages using coupled n-RRR planar parallel mechanisms, achieving extremely low parasitic shifts while addressing the forementioned limitations. Four design variants are selected and analyzed via Finite Element Method (FEM) simulations, evaluating parasitic shifts, stroke, and support stiffness. The most precise configuration, a 4-RRR rectilinear stage having kinematic chains coupled via two Watt linkages, exhibits a lateral shift smaller than 0.258 µm and an in-plane parasitic rotation smaller than 12.6 µrad over a 12 mm stroke. Experimental validation using a POM prototype confirms the high positioning precision and support stiffness properties. In addition, a silicon prototype incorporating thermally preloaded buckling beams is investigated to reduce its translational stiffness. Experimental results show a translational stiffness reduction of 98% in the monostable configuration and 112% in the bistable configuration (i.e., negative stiffness), without support stiffness reduction. These results highlight the potential of the proposed mechanisms for a wide range of precision applications, offering a scalable and high-accuracy solution for micro- and nano-positioning systems. Full article

(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics)

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21 pages, 4416 KB

Open AccessEditor’s ChoiceArticle

A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment

by Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen and Pingyu Jiang

Machines 2025, 13(6), 522; https://doi.org/10.3390/machines13060522 - 16 Jun 2025

Viewed by 909

Abstract

Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1) [...] Read more.

Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1) existing DT modeling methods are usually focused on specific types of equipment rather than being generally applicable to different types of equipment and requirements. (2) Existing DT models usually emphasize working condition monitoring and have relatively limited capability for modeling the operation and maintenance mechanism of the equipment for further decision making. (3) How to integrate artificial intelligence algorithms into DT models still requires further exploration. In this regard, a systematic and general DT modeling method is proposed for the remote monitoring and intelligent maintenance of industrial equipment. The DT model contains a multi-modal digital model, a multi-layer status model, and an intelligent interaction model driven by a kind of human-readable/computer-deployable event-state knowledge graph. Using the model, the dynamic workflows, working mechanisms, working status, workpiece logistics, monitoring data, and intelligent functions, etc., during the remote monitoring and maintenance of industrial equipment can be realized. The model was verified through three different DT modeling scenarios of a robot-based carbon block polishing processing line. Full article

(This article belongs to the Special Issue Intelligent Fault Detection and Diagnosis in Condition-Based Maintenance)

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51 pages, 13105 KB

Open AccessEditor’s ChoiceReview

Current Status and Trends of Wall-Climbing Robots Research

by Shengjie Lou, Zhong Wei, Jinlin Guo, Yu Ding, Jia Liu and Aiguo Song

Machines 2025, 13(6), 521; https://doi.org/10.3390/machines13060521 - 15 Jun 2025

Cited by 2 | Viewed by 5818

Abstract

A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as [...] Read more.

A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as inspection, cleaning, maintenance, and rescue while maintaining stable adhesion to the surface. Its applications span various sectors, including industrial maintenance, marine engineering, and aerospace manufacturing. This paper provides a systematic review of the physical principles and scalability of various attachment methods used in wall-climbing robots, with a focus on the applicability and limitations of different attachment mechanisms in relation to robot size and structural design. For specific attachment methods, the design and compatibility of motion and attachment mechanisms are analyzed to offer design guidance for wall-climbing robots tailored to different operational tasks. Additionally, this paper reviews localization and path planning methods for wall-climbing robots, comparing graph search, sampling-based, and feedback-based algorithms to guide strategy selection across varying environments and tasks. Finally, this paper outlines future development trends in wall-climbing robots, including the diversification of locomotion mechanisms, hybridization of attachment systems, and advancements in intelligent localization and path planning. This work provides a comprehensive theoretical foundation and practical reference for the design and application of wall-climbing robots. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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23 pages, 29181 KB

Open AccessEditor’s ChoiceArticle

Design and Implementation of a Bionic Marine Iguana Robot for Military Micro-Sensor Deployment

by Gang Chen, Xin Tang, Baohang Guo, Guoqi Li, Zhengrui Wu, Weizhe Huang, Yidong Xu, Ming Lu, Jianfei Liang and Zhen Liu

Machines 2025, 13(6), 505; https://doi.org/10.3390/machines13060505 - 9 Jun 2025

Cited by 1 | Viewed by 1771

Abstract

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective [...] Read more.

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering. Full article

(This article belongs to the Special Issue Design and Application of Bionic Robots)

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31 pages, 1997 KB

Open AccessEditor’s ChoiceReview

The Application of Reinforcement Learning to Pumps—A Systematic Literature Review

by Adetoye Ayokunle Aribisala, Usama Ali Salahuddin Ghori and Cristiano A. V. Cavalcante

Machines 2025, 13(6), 480; https://doi.org/10.3390/machines13060480 - 3 Jun 2025

Cited by 2 | Viewed by 3357

Abstract

Reinforcement learning, a subset of machine learning in the field of engineering informatics, has revolutionized the decision-making and control of industrial pumping systems. A set of 100 peer-reviewed papers on the application of reinforcement learning to pumps, sourced from the Scopus database, were [...] Read more.

Reinforcement learning, a subset of machine learning in the field of engineering informatics, has revolutionized the decision-making and control of industrial pumping systems. A set of 100 peer-reviewed papers on the application of reinforcement learning to pumps, sourced from the Scopus database, were selected. The selected papers were subjected to bibliometric and content analyses. The existing approaches in use, the challenges that have been experienced, and the future trends in the field are all explored in depth. The majority of the studies focused on developing a control system for pumps, with heat pumps being the most prevalent type, while also considering their economic impact on energy consumption in the industry. Future trends include the use of Internet-of-Things sensors on pumps, a hybrid of model-free and model-based reinforcement learning algorithms, and the development of “weighted” models. Finally, ideas for developing a practical reinforcement learning-bundled software for the industry are presented to create an effective system that includes a comprehensive reinforcement learning framework application. Full article

(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)

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21 pages, 4286 KB

Open AccessEditor’s ChoiceArticle

Digital Twin-Driven Condition Monitoring System for Traditional Complex Machinery in Service

by Weiming Yin, Yefa Hu, Guoping Ding and Xuefei Chen

Machines 2025, 13(6), 464; https://doi.org/10.3390/machines13060464 - 27 May 2025

Viewed by 1617

Abstract

Improvement in the intelligence and reliability of traditional complex machinery in service (TCMIS) is a prerequisite to guarantee the safety and stable production of these manufacturing enterprises. Existing studies on condition monitoring of TCMIS typically suffer from an insufficient volume of data, incomplete [...] Read more.

Improvement in the intelligence and reliability of traditional complex machinery in service (TCMIS) is a prerequisite to guarantee the safety and stable production of these manufacturing enterprises. Existing studies on condition monitoring of TCMIS typically suffer from an insufficient volume of data, incomplete consideration of issues, low monitoring accuracy, and lack of long-term validity. This paper proposes to utilize Digital Twin (DT) technology to construct a new generation of intelligent condition monitoring systems and take the coal mill of a coal-fired power plant as an example for practical illustration. The results of the study show that the method used in this paper is 96% for fault diagnosis, which is higher than the level in existing studies, and the practical application effect in coal-fired power plants also proves the effectiveness of this study. This study can provide program references for the development of intelligent transformation of TCMIS, and also provide technical support for the application and promotion of DT technology in this field. Full article

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

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24 pages, 3679 KB

Open AccessEditor’s ChoiceArticle

Design of JARI: A Robot to Enhance Social Interaction in Children with Autism Spectrum Disorder

by Ericka Patricia Madrid Ruiz, Héctor Hugo Oscanoa Fernández, Cecilia E. García Cena and Raquel Cedazo León

Machines 2025, 13(5), 436; https://doi.org/10.3390/machines13050436 - 21 May 2025

Cited by 4 | Viewed by 2394

Abstract

Robots designed for children with Autism Spectrum Disorder (ASD) have demonstrated potential in promoting social engagement and emotional learning. This study presents the design and preliminary evaluation of JARI, a social robot developed to support emotional recognition and interaction in children with ASD [...] Read more.

Robots designed for children with Autism Spectrum Disorder (ASD) have demonstrated potential in promoting social engagement and emotional learning. This study presents the design and preliminary evaluation of JARI, a social robot developed to support emotional recognition and interaction in children with ASD aged 6 to 8 years. The robot integrates mechanical, electronic, and software components within a modular architecture and is operated via a web-based Wizard of Oz interface. Aesthetic decisions, including a deliberately ambiguous zoomorphic appearance to avoid triggering the recognition of specific animal forms and the use of sensory accessories, were made to increase acceptance and reduce overstimulation. JARI was tested in the following two scenarios: individual interaction at a special education center in Peru, and group interaction at an inclusive school in Spain. Results show that most children were able to identify the robot’s emotional expressions and responded positively to its color cues. Behavioral analysis revealed significant engagement through physical gestures, sustained visual attention, and emotional mirroring. These findings suggest that JARI is effective in capturing attention and eliciting meaningful interaction from children with ASD. Full article

(This article belongs to the Special Issue Design and Control of Assistive Robots)

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26 pages, 5325 KB

Open AccessEditor’s ChoiceArticle

Hybrid Damping Mode MR Damper: Development and Experimental Validation with Semi-Active Control

by Jeongwoo Lee and Kwangseok Oh

Machines 2025, 13(5), 435; https://doi.org/10.3390/machines13050435 - 20 May 2025

Cited by 5 | Viewed by 2475

Abstract

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This [...] Read more.

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article

(This article belongs to the Special Issue Adaptive Control Using Magnetorheological Technology)

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29 pages, 7349 KB

Open AccessEditor’s ChoiceArticle

Dynamic Error Compensation for Ball Screw Feed Drive Systems Based on Prediction Model

by Hongda Liu, Yonghao Guo, Jiaming Liu and Wentie Niu

Machines 2025, 13(5), 433; https://doi.org/10.3390/machines13050433 - 20 May 2025

Cited by 1 | Viewed by 1183

Abstract

The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based [...] Read more.

The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based on the lumped and distributed mass methods, this method constructs a parameterized dynamic model relying on the moving component’s position for electromechanical coupling modeling. Using Latin Hypercube Sampling and numerical simulation, a sample set containing the input and output of one control cycle is obtained, which is used to train a Cascade-Forward Neural Network to predict dynamic errors. Finally, a feedforward compensation strategy based on the prediction model is proposed to improve tracking performance. The proposed method is applied to a ball screw feed system. Tracking error simulations and experiments are conducted and compared with the transfer function feedforward compensation. Typical trajectories are designed to validate the effectiveness of the electromechanical coupling model, the dynamic error prediction model, and the feedforward compensation strategy. The results show that the prediction model exhibits a maximum prediction deviation of 1.8% for the maximum tracking error and 13% for the average tracking error. The proposed compensation method with friction compensation achieves a maximum reduction rate of 76.7% for the maximum tracking error and 63.7% for the average tracking error. Full article

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

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21 pages, 16495 KB

Open AccessEditor’s ChoiceArticle

Tactile Force Sensing for Admittance Control on a Quadruped Robot

by Thijs Van Hauwermeiren, Annelies Coene and Guillaume Crevecoeur

Machines 2025, 13(5), 426; https://doi.org/10.3390/machines13050426 - 19 May 2025

Viewed by 1472

Abstract

Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to [...] Read more.

Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to modeling errors. In contrast, direct sensing of contact forces at the feet provides more accurate and immediate feedback. Beyond force magnitude, tactile sensing also enables richer contact interpretation, such as detecting force direction and surface properties. In this work, we show how tactile sensor information can be used inside the feedback of the control loop to achieve compliance of legged robots during ground contact. The three main contributions are (i) a fast and computationally efficient 3D force reconstruction method tailored for spherical tactile sensors, (ii) a tactile admittance controller that adjusts leg motions to achieve the desired GRFs and compliance, and (iii) experimental validation on a quadruped robot, demonstrating enhanced load distribution and balance during external perturbations and locomotion. The results show that the peak ground reaction forces were reduced by 55% while balancing on a beam. During a locomotion scenario involving sudden touchdown after a fall, the tactile admittance controller reduced oscillations and regained stability compared to proportional–derivative (PD) control. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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23 pages, 6942 KB

Open AccessEditor’s ChoiceArticle

Research on the Dynamic Response of a Cracked-Spur Gear System with Parameter Uncertainty

by Ping Fang, Yang Yang and Jin Zeng

Machines 2025, 13(5), 395; https://doi.org/10.3390/machines13050395 - 9 May 2025

Viewed by 542

Abstract

In this paper, the time-varying mesh stiffness (TVMS) of a gear is meticulously derived using the potential energy method (PEM) and an analytical expression for it is obtained. Subsequently, calculations are performed to determine the effects of crack depth and crack angle on [...] Read more.

In this paper, the time-varying mesh stiffness (TVMS) of a gear is meticulously derived using the potential energy method (PEM) and an analytical expression for it is obtained. Subsequently, calculations are performed to determine the effects of crack depth and crack angle on the TVMS. The validation is carried out using the finite element method (FEM). Then, a discussion is carried out on the dynamic characteristics of a spur gear system with a crack. Moreover, uncertainty is an objective reality in gear systems, arising from various factors such as the material properties and working environment. To enable a more reasonable evaluation of the dynamic characteristics of the spur gear system, this paper presents a deviation of an uncertainty interval analysis method based on Chebyshev polynomials. A dynamic model of the spur gear system with uncertain parameters is then proposed. The dynamic response of a gear transmission system with these uncertain parameters is investigated in detail. Additionally, the interval response of a gear system with root cracks under uncertainty is further investigated. The experimental results confirm the inherent presence of uncertainty in the gear system and validate the effectiveness of the proposed uncertainty analysis method. Full article

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

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18 pages, 5368 KB

Open AccessEditor’s ChoiceArticle

UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES

by Shihai Cao, Ting Wang, Tao Li and Shumin Fei

Machines 2025, 13(5), 364; https://doi.org/10.3390/machines13050364 - 28 Apr 2025

Cited by 1 | Viewed by 1486

Abstract

In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or [...] Read more.

In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to

37.5

. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods. Full article

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

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15 pages, 5477 KB

Open AccessEditor’s ChoiceArticle

Simulated Centrifugal Fan Blade Fault Diagnosis Based on Modulational Depthwise Convolution–One-Dimensional Convolution Neural Network (MDC-1DCNN) Model

by Zhaohui Ren, Yulin Liu, Tianzhuang Yu, Shihua Zhou, Yongchao Zhang and Zeyu Jiang

Machines 2025, 13(5), 356; https://doi.org/10.3390/machines13050356 - 24 Apr 2025

Viewed by 869

Abstract

Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex [...] Read more.

Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex and harsh environments such as boiler plants and gas turbines. Therefore, the vibration signals they generate show complex and diverse characteristics, which brings great challenges to the monitoring of centrifugal fan operation status. To solve this problem, this paper proposes a centrifugal fan blade fault diagnosis method based on a modulational depthwise convolution (DWconv)–one-dimensional convolution neural network (MDC-1DCNN). Specifically, firstly, a convolutional modulation module (CMM) with strong local perception and global modeling capability is designed by drawing on the Transformer self-attention mechanism and global context modeling idea. Second, multiple DWconv layers of different sizes are introduced to capture high-frequency shocks and low-frequency fluctuation information of different frequencies and durations in the signal. Next, a DWconv layer of size 11 is embedded in the multilayer perceptron to enhance spatial information representation while saving computational resources. Finally, to verify the effectiveness of the method, this paper simulates and analyzes the actual working state of centrifugal fan blades, constructs a simulation dataset, and builds a centrifugal fan experimental bench to obtain a real dataset. The experimental results show that the MDC-1DCNN framework significantly outperforms the existing methods in both simulation and experimental bench datasets, fully proving its versatility and effectiveness in centrifugal fan blade fault diagnosis. Full article

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

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20 pages, 5784 KB

Open AccessEditor’s ChoiceArticle

Lower Limb Motion Recognition Based on Surface Electromyography Decoding Using S-Transform Energy Concentration

by Baoyu Li, Guanghua Xu, Jinju Pei, Dan Luo, Hui Li, Chenghang Du, Kai Zhang and Sicong Zhang

Machines 2025, 13(5), 346; https://doi.org/10.3390/machines13050346 - 23 Apr 2025

Cited by 1 | Viewed by 1280

Abstract

Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, [...] Read more.

Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, the approach combines S-transform energy concentration and multi-channel fusion analysis. EMG signals from six lower limb muscles of 10 subjects performing four movements (level walk, stair ascent, stair descent, and obstacle crossing) were analyzed. Correlation analysis identified the most relevant and least correlated muscles, optimizing signal quality. Using support vector machines (SVM), motion recognition accuracy was evaluated for single-channel and multi-channel signals. Results indicated that the semi-tendon and rectus femoris muscles achieved 80.71% accuracy with simple time-frequency features, while the medial gastrocnemius and rectus femoris reached 93.70% accuracy with S-transform energy concentration. Multi-channel fusion (rectus femoris, biceps femoris, and medial gastrocnemius) based on S-transform achieved over 96% accuracy, demonstrating superior recognition performance and potential for improving adaptive human-robot interaction in prosthetic control. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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20 pages, 10930 KB

Open AccessEditor’s ChoiceArticle

Development of the E-Portal for the Design of Freeform Varifocal Lenses Using Shiny/R Programming Combined with Additive Manufacturing

by Negin Dianat, Shangkuan Liu, Kai Cheng and Kevin Lu

Machines 2025, 13(4), 298; https://doi.org/10.3390/machines13040298 - 3 Apr 2025

Viewed by 944

Abstract

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near [...] Read more.

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near distances. The user interface (UI) of the e-portal application creates an environment for customers to input their eye prescription data and geometric parameters to visualise the result of the designed freeform varifocal lens surface, which includes interactive 2D contour plots and 3D-rendered diagrams for both left and right eyes simultaneously. The e-portal provides a unified interactive platform where users can simultaneously access both the specialised Copilot demo web for lenses and the main Shiny/R version 4.4.0 programming app, ensuring seamless integration and an efficient process flow. Additionally, the data points of the 3D-designed surface are automatically saved. In order to check the performance of the designed varifocal lens before production, it is remodelled in the COMSOL Multiphysics 6.2 modelling and analysis environment. Ray tracing is built in the environment for the lens design assessment and is then integrated with the lens additive manufacturing (AM) using a Formlabs 3D printer (Digital Fabrication Center (DFC), London, UK). The results are then analysed to further validate the e-portal-driven personalised design and manufacturing approach. Full article

(This article belongs to the Section Advanced Manufacturing)

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26 pages, 46550 KB

Open AccessEditor’s ChoiceArticle

A Novel Ground-to-Elevated Mobile Manipulator Base System for High-Altitude Operations

by Hongjia Wu, Chengzhang Gong, Li Fan, Guoan Liu, Yonghuang Zheng, Tingzheng Shen and Xiangbo Suo

Machines 2025, 13(4), 288; https://doi.org/10.3390/machines13040288 - 31 Mar 2025

Viewed by 927

Abstract

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the [...] Read more.

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

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

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16 pages, 4218 KB

Open AccessEditor’s ChoiceArticle

Method for Determining Displacement and Characterizing Spatial Kinematic Misalignment in a Rigid Exoskeleton

by Oliver Ott, Samet Ersoysal, Niklas Kraus and Robert Weidner

Machines 2025, 13(4), 284; https://doi.org/10.3390/machines13040284 - 30 Mar 2025

Viewed by 938

Abstract

With the increasing use of exoskeletons to reduce physical strain in industrial applications, precise adaptation to the user’s anthropometry is crucial for effective force transmission and user acceptance. This paper presents a method that compensates for axial misalignment between the human joint and [...] Read more.

With the increasing use of exoskeletons to reduce physical strain in industrial applications, precise adaptation to the user’s anthropometry is crucial for effective force transmission and user acceptance. This paper presents a method that compensates for axial misalignment between the human joint and the exoskeleton’s axes of rotation to optimize the anthropometric alignment. It further quantifies the resulting displacement, providing instructional recommendations for manual refinement of the exoskeleton’s initial kinematic configuration. The method thereby represents a first step toward a comprehensive investigation of the initial offset’s influence on an anthropometric fit. The mathematical derivation was described using the rigid shoulder exoskeleton “Lucy”. The validation on increasingly complex mock-ups showed an average calculated error of

0.84 mm

(SD

0.24 mm

) in 2D and

7.97 mm

(SD

1.30 mm

) in 3D, where the errors decreased with smaller initial offsets. A preliminary field study with three participants revealed improved anthropometric alignment but indicated limitations in the exoskeleton’s structural adjustment possibilities, highlighting the need for further modifications. Building on these findings, subsequent studies will involve further investigation of factors such as the migration of the instantaneous center of rotation during motion, soft tissue deformations, and greater population diversity. Full article

(This article belongs to the Special Issue Design and Control of Wearable Mechatronics Devices)

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25 pages, 16138 KB

Open AccessEditor’s ChoiceArticle

Tool Condition Monitoring in the Milling of Low- to High-Yield-Strength Materials

by Sohan Nagaraj and Nancy Diaz-Elsayed

Machines 2025, 13(4), 276; https://doi.org/10.3390/machines13040276 - 27 Mar 2025

Cited by 1 | Viewed by 1550

Abstract

The preservation and continuous monitoring of cutting tools in a computer numerical control (CNC) machine is essential for ensuring seamless transitions in the manufacturing workflow, as well as maintaining adequate part quality. The implementation of tool condition monitoring (TCM) when milling can provide [...] Read more.

The preservation and continuous monitoring of cutting tools in a computer numerical control (CNC) machine is essential for ensuring seamless transitions in the manufacturing workflow, as well as maintaining adequate part quality. The implementation of tool condition monitoring (TCM) when milling can provide the user with necessary data regarding tool life, wear, and part quality. However, it is important to broaden the application of the TCM process across a much broader class of workpiece materials to understand the effects of material properties on the condition of the tool. The aim of this paper is to investigate the efficacy of tool condition monitoring techniques while milling low- and high-yield-strength materials across varied process parameters. A Fast Fourier Transform (FFT) analysis was conducted in this research. Vibration data were acquired from both uniaxial and triaxial accelerometers to investigate irregularities in vibrational amplitudes between new and worn milling tools. The experimental results show that there is a significant increase in vibrational amplitudes for the worn tool when compared to the new tool across various frequencies, which affirms the expected increase in vibrations and cutting forces at the tool–workpiece interface from using a worn tool. The F-values and p-values calculated using an F-test with a 95% confidence interval indicated statistically significant differences in vibration data between new and worn tools across various materials, including polyurethane foam, aluminum 6061, mild steel, and stainless steel, under different cutting conditions (low, medium, and high). These results further validate the findings obtained from the FFT analysis and highlight the effectiveness of vibration-based monitoring in distinguishing tool wear under varying material characteristics and machining conditions. Full article

(This article belongs to the Special Issue Innovations in the Design, Simulation, and Manufacturing of Production Systems)

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21 pages, 15017 KB

Open AccessEditor’s ChoiceArticle

End-to-End Intelligent Adaptive Grasping for Novel Objects Using an Assistive Robotic Manipulator

by Zhangchi Ding, Amirhossein Jabalameli, Mushtaq Al-Mohammed and Aman Behal

Machines 2025, 13(4), 275; https://doi.org/10.3390/machines13040275 - 26 Mar 2025

Viewed by 1004

Abstract

This paper presents the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on extensive user studies of the system, several features were implemented in the interface that could [...] Read more.

This paper presents the design and implementation of the motion controller and adaptive interface for the second generation of the UCF-MANUS intelligent assistive robotic manipulator system. Based on extensive user studies of the system, several features were implemented in the interface that could reduce the complexity of the human–robot interaction while also compensating for the deficits in different human factors, such as working memory, response inhibition, processing speed, depth perception, spatial awareness, and contrast sensitivity. To effectively and safely control the robotic arm, we designed several new features, including an adaptive human–robot interaction framework. To provide the user with a less complex and safer interaction with the robot, we added new functionalities such as ‘One-click mode’, ‘Move suggestion mode’, and ‘Gripper Control Assistant’. Furthermore, to equip our assistive robotic system with an adaptive User Interface, we designed and implemented compensators such as ‘Contrast Enhancement’, ‘Object Proximity Velocity Reduction’, and ‘Orientation Indicator’. Results from a multitude of experiments show that the system is indeed robust, safe, and computationally efficient in addition to addressing the user’s highly desired capabilities. Full article

(This article belongs to the Special Issue Advances in Assistive Robotics)

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