Machines

19 pages, 4921 KiB

Open AccessArticle

Sports Biomechanics Analysis: Assisting Effectiveness Evaluations for Wearable Compliant Elbow Joint Powered Exoskeleton

by Huibin Qin, Kai Liu, Zefeng Zhang, Jie Zheng, Zhili Hou, Lina Li and Ruiqin Li

Machines 2025, 13(2), 168; https://doi.org/10.3390/machines13020168 - 19 Feb 2025

Viewed by 693

Abstract

Wearing an exoskeleton, the human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities such as manual material handling. Currently, exoskeleton systems are commonly integrated with sensor technologies to gather data and [...] Read more.

Wearing an exoskeleton, the human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities such as manual material handling. Currently, exoskeleton systems are commonly integrated with sensor technologies to gather data and assess performances. This is mainly performed to evaluate the physical exoskeletons, and cannot provide real-time feedback during the development phase. Firstly, a powered wearable elbow exoskeleton with variable stiffness is proposed. Through theoretical calculation, the power efficiency formula of exoskeleton is derived. Then, a human musculoskeletal model is built using the AnyBody Modeling System and coupled to the elbow exoskeleton. Under set experimental conditions, the simulation reveals that, when compared with the exoskeleton, the biceps and triceps muscle force parameters of the human model were reduced by 24% and 12%. The muscle activity was diminished by 28–31%, and muscle length shortened by about 6%, in comparison to the condition without the exoskeleton. Finally, through the muscle force experiment, it was verified that the power efficiency of the elbow exoskeleton in the real transport was about 18%. The project reduces costs in the development phase of the exoskeleton and can provide new insights into muscle function and sports biomechanics. Full article

(This article belongs to the Special Issue Compliant Mechanisms and Actuators for Exoskeletons and Human–Robot Interactions)

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13 pages, 10799 KiB

Open AccessArticle

Development of a Bicycle-like Magnetic-Wheeled Climbing Robot with Adaptive Plane-Transition Capabilities

by Yongjian Bu, Lide Dun, Yongtao Deng, Bingdong Jiang, Aihua Jiang and Haifei Zhu

Machines 2025, 13(2), 167; https://doi.org/10.3390/machines13020167 - 19 Feb 2025

Cited by 1 | Viewed by 377

Abstract

Although robots are increasingly expected to perform inspection tasks in three-dimensional ferromagnetic structural environments, magnetic-wheeled climbing robots face significant challenges in overcoming obstacles and transiting between planes. In this paper, we propose a novel bicycle-like magnetic-wheeled climbing robot, named BiMagBot, featuring two magnetic [...] Read more.

Although robots are increasingly expected to perform inspection tasks in three-dimensional ferromagnetic structural environments, magnetic-wheeled climbing robots face significant challenges in overcoming obstacles and transiting between planes. In this paper, we propose a novel bicycle-like magnetic-wheeled climbing robot, named BiMagBot, featuring two magnetic wheels that allow the adaptive adjustment of magnetic adhesion without the need for active control. The front wheel incorporates an arc tentacle mechanism that rotates a ring magnet to adjust the magnetic adhesion, while the rear wheel uses an eccentric shaft-hole design to facilitate a smooth transition of magnetic adhesion between surfaces. The magnetic forces acting on both wheels during transitions through concave corners were analyzed and discussed via simulations to elucidate the underlying principles. A prototype of the robot was developed and tested experimentally. The results show that the front and rear wheels can adjust the magnetic adhesion during the transition of corners with angles ranging from 90° to 315°. The robot only weighs 1.6 kg, but it can carry a weight of 2 kg with a speed of 0.9 m/s to transit across concave corners, demonstrating comprehensive capabilities in plane transition, ease of control, and load capacity. Full article

(This article belongs to the Special Issue Climbing Robots: Scaling Walls with Precision and Efficiency)

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25 pages, 4205 KiB

Open AccessArticle

Method of Dynamic Modeling and Robust Optimization for Chain Transmission Mechanism with Time-Varying Load Uncertainty

by Taisu Liu, Yuan Liu, Peitong Liu and Xiaofei Du

Machines 2025, 13(2), 166; https://doi.org/10.3390/machines13020166 - 19 Feb 2025

Viewed by 414

Abstract

Time-varying driving loads and uncertain structural parameters affect the transmission accuracy of chain transmission mechanisms. To enhance the transmission accuracy and placement consistency of these mechanisms, a robust optimization design method based on Karhunen–Loeve expansion and Polynomial Chaos Expansion (KL-PCE) is proposed. First, [...] Read more.

Time-varying driving loads and uncertain structural parameters affect the transmission accuracy of chain transmission mechanisms. To enhance the transmission accuracy and placement consistency of these mechanisms, a robust optimization design method based on Karhunen–Loeve expansion and Polynomial Chaos Expansion (KL-PCE) is proposed. First, a dynamic model of the chain transmission mechanism, considering multiple contact modes, is established, and the model’s accuracy is verified through experiments. Then, based on the KL-PCE method, a mapping relationship between uncertain input parameters and output responses is established. A robust optimization design model for the chain transmission process is formulated, with transmission accuracy and consistency as objectives. Finally, case studies are used to verify the effectiveness of the proposed method. Thus, the transmission accuracy of the chain transmission mechanism is improved, providing a theoretical foundation for the design of chain transmission mechanisms under time-varying load uncertainties and for improving the accuracy of other complex mechanisms. Full article

(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements)

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28 pages, 17813 KiB

Open AccessArticle

Research on Operation Trajectory Tracking Control of Loader Working Mechanisms

by Guodong Liang, Yong Jiang, Zeyu Gao, Guoxing Bai, Hengtong Li, Xiaoyan Zhao, Kai Wang and Zhiyan Wang

Machines 2025, 13(2), 165; https://doi.org/10.3390/machines13020165 - 19 Feb 2025

Viewed by 461

Abstract

Autonomous shovel digging of loaders is the key technology to realise automation and intelligent operation. The effective tracking control for the target operation trajectory is one of its core parts. Proportional–integral–derivative (PID) and other control methods without system models have issues, such as [...] Read more.

Autonomous shovel digging of loaders is the key technology to realise automation and intelligent operation. The effective tracking control for the target operation trajectory is one of its core parts. Proportional–integral–derivative (PID) and other control methods without system models have issues, such as large overshoot amplitudes and jitter phenomena under system constraints. Given that model predictive control (MPC) effectively deals with system constraints to ensure smooth operation, this paper introduces MPC into motion control for the loader’s working mechanism and proposes a trajectory tracking control method based on nonlinear model predictive control (NMPC). This study shows that, under the same system constraints for different target operation trajectories, the designed controller achieves better tracking performance than conventional PID and sliding-mode control (SMC) controllers in handling system constraints and ensuring smoothness. It is also found that the tracking performance decreases as the dig insertion depth increases. Therefore, trajectories with larger dig insertion depths are not recommended as viable operation trajectories. This study provides an important foundation and new insights for improving the control performance of the loader’s working mechanism. Full article

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

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27 pages, 12290 KiB

Open AccessArticle

An Improved IPMSM Discrete-Time Nonlinear Model for Hardware-in-the-Loop Test Systems

by Yingpeng Fan, Guoqing Zhu and Jian Luo

Machines 2025, 13(2), 164; https://doi.org/10.3390/machines13020164 - 19 Feb 2025

Viewed by 323

Abstract

Interior permanent magnet synchronous motors (IPMSMs) exhibit significant nonlinear electromagnetic behaviour due to the effects of saturation, cross-coupling, spatial harmonics, temperature, and iron losses. In order to effectively capture the actual electromagnetic behaviour of IPMSMs, this paper proposes an improved IPMSM nonlinear model. [...] Read more.

Interior permanent magnet synchronous motors (IPMSMs) exhibit significant nonlinear electromagnetic behaviour due to the effects of saturation, cross-coupling, spatial harmonics, temperature, and iron losses. In order to effectively capture the actual electromagnetic behaviour of IPMSMs, this paper proposes an improved IPMSM nonlinear model. The proposed model is based on the nonlinear flux-linkage model and progressively incorporates the effects of spatial harmonics, temperature, and iron losses. In this paper, the discrete-time form of the improved nonlinear model is established directly. It is suitable not only for embedding into the Matlab/Simulink environment as an alternative to field circuit coupling simulation but also for deployment into field programmable gate arrays (FPGA) as the model basis for hardware-in-the-loop testing. The effectiveness and feasibility of the improved model are verified by experimental results. Full article

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

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20 pages, 718 KiB

Open AccessArticle

Closed-Loop Transient Longitudinal Trajectory Tracking for Connected Vehicles

by Lingyun Hua and Guoming Zhu

Machines 2025, 13(2), 163; https://doi.org/10.3390/machines13020163 - 19 Feb 2025

Cited by 1 | Viewed by 321

Abstract

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In [...] Read more.

Vehicle longitudinal trajectory tracking plays a significant role in developing ecorouting and autonomous driving systems to handle various disturbances and uncertainties (e.g., road grade, gust wind, etc.) that are often ignored by the optimization strategies used to generate reference controls and trajectories. In this paper, based on a linearized vehicle model with the help of feedback linearization, a linear quadratic integral tracking (LQIT) control is utilized to generate regulation laws to minimize the tracking error of optimal speed or brake distance trajectories, respectively, and maintain brake safety. A unified Kalman filter is used to estimate system states based on noisy measurements. Both acceleration and deceleration LQIT controls are designed to handle the change of upperlevel optimal control strategies to varying traffic. Simulation and co-simulation studies validated the proposed LQIT control strategies in Simulink with the SUMO traffic model using a real-world map under manipulated driving conditions. The simulation results show that under changing traffic conditions, the LQIT acceleration control is able to reduce the static tracking error by 99.8%, compared with the vehicle controlled only by the high-level optimal acceleration control without a trajectory tracker, achieving less tracking error and overshoot than using a PI control. The LQIT deceleration control reduces the brake distance error by 48% over the optimal deceleration control alone and ensures a safer brake distance than a coupled PI control. The traffic model used in the SUMO co-simulation confirms the capability of handling varying traffic for the developed LQIT control strategies. Full article

(This article belongs to the Section Vehicle Engineering)

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29 pages, 4506 KiB

Open AccessArticle

Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning

by Jiandong Liu, Wei Luo, Guoqing Zhang and Ruihao Li

Machines 2025, 13(2), 162; https://doi.org/10.3390/machines13020162 - 18 Feb 2025

Viewed by 1077

Abstract

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a [...] Read more.

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target. Full article

(This article belongs to the Special Issue Flight Control and Path Planning of Unmanned Aerial Vehicles)

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32 pages, 22165 KiB

Open AccessArticle

Reshaping Load-Dependent Mesh Excitation Waveforms of Spur Gears—An Analytical Framework on Tip Relief Modeling and Design

by Xingyuan Zheng, Weidong Zhu, Gang Li and Yumei Hu

Machines 2025, 13(2), 161; https://doi.org/10.3390/machines13020161 - 18 Feb 2025

Cited by 1 | Viewed by 390

Abstract

Tip relief is a critical design feature of modern spur gears, aimed at improving dynamic performance through a typical design strategy involving peak-to-peak minimization of mesh excitations. However, due to the hyperstatic nature of simultaneous tooth engagements, the applied torque not only affects [...] Read more.

Tip relief is a critical design feature of modern spur gears, aimed at improving dynamic performance through a typical design strategy involving peak-to-peak minimization of mesh excitations. However, due to the hyperstatic nature of simultaneous tooth engagements, the applied torque not only affects mesh deformation amplitudes as normally considered but also alters mesh excitation waveforms, leaving great challenges for the typical design to meet various operating conditions. This paper develops an analytical framework to reshape mesh excitation waveforms, aimed at flexibly reducing vibration intensities across different operating loads and speeds. The load-dependency of excitation harmonics with tip relief is efficiently characterized by an improved analytical mesh excitation model. A tip relief design method is proposed, which automatically recombines harmonic contents of mesh excitations to adapt target operating speeds. Comparisons with finite element models and experiments confirmed the accuracies of quasi-static and dynamic analyses. Parametric studies and application examples further demonstrate the acceptable feasibility and effectiveness of the present method. Full article

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

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45 pages, 3966 KiB

Open AccessReview

A Comprehensive Study of Cooling Rate Effects on Diffusion, Microstructural Evolution, and Characterization of Aluminum Alloys

by Atiqur Rahman, Sriram Praneeth Isanaka and Frank Liou

Machines 2025, 13(2), 160; https://doi.org/10.3390/machines13020160 - 18 Feb 2025

Viewed by 1345

Abstract

Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study [...] Read more.

Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study explores the impact of CRs in diffusion, microstructural development, and the characterization of aluminum alloys and the influence of various manufacturing processes and post-process treatments, and it studies analytical models that can predict their effects. It examines a broad range of CRs encountered in diverse manufacturing methods, such as laser powder bed fusion (LPBF), directed energy deposition (DED), casting, forging, welding, and hot isostatic pressing (HIP). For example, varying CRs might result in different types of solidification and microstructural evolution in aluminum alloys, which thereby influence their mechanical properties during end use. The study further examines the effects of post-process heat treatments, including quenching, annealing, and precipitation hardening, on the microstructure and mechanical properties of aluminum alloys. It discusses numerical and analytical models, which are used to predict and optimize CRs for achieving targeted material characteristics of specific aluminum alloys. Although understanding CR and its effects is crucial, there is a lack of literature on how CR affects alloy properties. This comprehensive review aims to bridge the knowledge gap through a thorough literature review of the impact of CR on microstructure and mechanical properties. Full article

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

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27 pages, 2579 KiB

Open AccessArticle

Assembly Quality Control Technologies in Forced Clamping and Compensation Processes for Large and Integrated Aeronautical Composite Structures

by Feiyan Guo, Qiangwei Bao, Jialiang Liu and Xiliang Sha

Machines 2025, 13(2), 159; https://doi.org/10.3390/machines13020159 - 18 Feb 2025

Cited by 1 | Viewed by 470

Abstract

For the new type of CFRP (Carbon Fiber Reinforced Plastic) thin-walled components with a large size and weak rigid structure, due to the integration of geometric features and the reduction in the amount of parts, the assembly size transmission chain is short compared [...] Read more.

For the new type of CFRP (Carbon Fiber Reinforced Plastic) thin-walled components with a large size and weak rigid structure, due to the integration of geometric features and the reduction in the amount of parts, the assembly size transmission chain is short compared to traditional metal assembly structures. In addition, the manufacturing errors and layer parameters of large composite parts in different regions are different, and they also have a lower forming accuracy. For the current assembly method that mainly concerns geometric dimensions and tolerances, it is difficult to support precise analysis and accurate geometric error forms for different local and global regions. As a result, in practical engineering, the forced method of applying a local clamping force is inevitably adopted to passively reduce and compensate for assembly errors. However, uneven stress distribution and possible internal damage occur. To avoid the assembly quality problems caused by forced clamping operations, the research status on the optimization of forced clamping process parameters before assembly, the flexible position–force adjustment of fixtures during assembly, and gap compensation and strengthening before assembly completion was analyzed systematically. The relevant key technologies, such as force limit setting, geometric gap reduction, stress/damage evolution prediction, the reverse optimization of clamping process parameters, and precise stress/damage measurement, are proposed and resolved in this paper. With the specific implementation solutions, geometric and mechanical assembly status coupling analysis, active control, and a collaborative guarantee could be achieved. Finally, future research work is proposed, i.e., dynamic evolution behavior modeling and the equalization of the induction and control of physical assembly states. Full article

(This article belongs to the Section Advanced Manufacturing)

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28 pages, 9558 KiB

Open AccessArticle

Economy Optimization by Multi-Strategy Improved Whale Optimization Algorithm Based on User Driving Cycle Construction for Hybrid Electric Vehicles

by Jie Ma, Mingzhang Pan, Wei Guan, Zhiqing Zhang, Jingcheng Zhou, Nianye Ye, Haifeng Qin, Lulu Li and Xingjia Man

Machines 2025, 13(2), 158; https://doi.org/10.3390/machines13020158 - 17 Feb 2025

Viewed by 412

Abstract

Nowadays, there is an increasing focus on enhancing the economy of hybrid electric vehicles (HEVs). This study builds a framework model for the parameter optimization of hybrid powertrains in user driving cycles. Unlike the optimization under standard driving cycles, the applied user driving [...] Read more.

Nowadays, there is an increasing focus on enhancing the economy of hybrid electric vehicles (HEVs). This study builds a framework model for the parameter optimization of hybrid powertrains in user driving cycles. Unlike the optimization under standard driving cycles, the applied user driving cycle incarnates realistic driving situations, and the optimization results are more realistic. Firstly, the user driving cycle with high accuracy is constructed based on actual driving data, which provides a basis for the performance analysis of HEV. Secondly, the HEV model with good power and economy is constructed under user driving cycles. Finally, a multi-strategy improved whale optimization algorithm (MIWOA) is proposed, which can guarantee better economy of HEV compared with the original whale optimization algorithm (WOA). The economy optimization of HEV is completed by MIWOA under user driving cycles, and the hybrid vehicle economy parameters that are more in line with the user’s actual driving conditions are obtained. After optimization, the 100 km equivalent fuel consumption (EFC) of HEV is reduced by 5.20%, which effectively improves the vehicle’s economy. This study demonstrates the effectiveness of the MIWOA method in improving economy and contributes a fresh thought and method for the economic optimization of the hybrid powertrain. Full article

(This article belongs to the Section Vehicle Engineering)

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19 pages, 2727 KiB

Open AccessArticle

Adaptive Sliding Mode Predictive Control for Path Tracking of Wheeled Agricultural Vehicles

by Wenlong Liu, Rui Guo and Jingyi Zhao

Machines 2025, 13(2), 157; https://doi.org/10.3390/machines13020157 - 17 Feb 2025

Cited by 1 | Viewed by 470

Abstract

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in [...] Read more.

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in order to design the path tracking controller by combining the precision advantage of model predictive control (MPC) algorithm with the robustness advantage of sliding mode control (SMC) algorithm, the sliding mode functions were designed and used as the output equations to establish the kinematics state space model of the vehicle. Thirdly, on the basis of linearization and discretization for the kinematics state space model, the control law of path tracking was obtained using the MPC algorithm. Finally, according to the fuzzy rules designed by the working speed of the vehicle and the curvature of the reference path, the prediction horizon and control horizon of the MPC algorithm were adaptively adjusted to further improve the control precision and robustness of the path tracking system. The results of CarSim and MATLAB/Simulink co-simulation show that the proposed ASMPC algorithm is superior to the traditional SMC algorithm and conventional MPC algorithm in terms of control precision, dynamic performance, and robustness. The results of our field test show that the root mean square (RMS) values of the lateral errors for straight path tracking and curve path tracking do not exceed 2.1 and 8.7 cm, respectively, in the speed range of 1.0 to 3.5 m/s, suitable for field working. The control precision and robustness of the proposed ASMPC algorithm can meet the working requirements of wheeled agricultural vehicles. Full article

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

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27 pages, 4776 KiB

Open AccessReview

Technical Roadmaps of Electric Motor Technology for Next Generation Electric Vehicles

by Adil Usman and Anchal Saxena

Machines 2025, 13(2), 156; https://doi.org/10.3390/machines13020156 - 17 Feb 2025

Viewed by 1295

Abstract

This paper provides a consolidated discussion and proposes significant measures in improving and advancing the performance of synchronous machines employed in electric traction applications designed for passenger electric vehicles (EVs). The paper quantifies the discussion on improving the power density (kW/kg) and efficiency [...] Read more.

This paper provides a consolidated discussion and proposes significant measures in improving and advancing the performance of synchronous machines employed in electric traction applications designed for passenger electric vehicles (EVs). The paper quantifies the discussion on improving the power density (kW/kg) and efficiency (%η) of the machine with the commercially available solutions in terms of new design architectures, advanced emerging materials, and adoption of additive manufacturing (AM) technologies. New challenges and opportunities are identified for the optimized machine designs having the potential to meet the global standards while keeping the cost under control. This paper provides an overview of current trends, an introduction to innovative technologies, and changes in existing manufacturing practices to achieve high-performance electrical machines with improved fault tolerance capabilities and reliability. Thereby meeting the standards for the next generation of electric vehicles. Full article

(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)

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26 pages, 6335 KiB

Open AccessArticle

Analysis of Nonlinear Dynamics of a Gear Transmission System Considering Effects of the Extended Tooth Contact

by Fulin Liao, Xingyuan Zheng, Jianliang Huang and Weidong Zhu

Machines 2025, 13(2), 155; https://doi.org/10.3390/machines13020155 - 17 Feb 2025

Viewed by 420

Abstract

Considering the elasticity of gear solid bodies, the load applied to gear teeth will force theoretically separated gear teeth to get into engaging state in advance. This phenomenon is named as the extended tooth contact (ETC). Effects of the ETC directly influence the [...] Read more.

Considering the elasticity of gear solid bodies, the load applied to gear teeth will force theoretically separated gear teeth to get into engaging state in advance. This phenomenon is named as the extended tooth contact (ETC). Effects of the ETC directly influence the time-varying mesh stiffness of gear pairs and subsequently alter nonlinear dynamic characteristics of gear transmission systems. Time-vary mesh stiffness, considering effects of the ETC, is thus introduced into the dynamic model of the gear transmission system. Periodic motions of a gear transmission system are discussed in detail in this work. The analytical model of time-varying mesh stiffness with effects of the ETC is proposed, and the effectiveness of the analytical model is demonstrated in comparison with finite element (FE) results. The gear transmission system is simplified as a single degree-of-freedom (DOF) model system by employing the lumped mass method. The correctness of the dynamic model is verified in comparison with experimental results. An incremental harmonic balance (IHB) method is modified to obtain periodic responses of the gear transmission system. The improved Floquet theory is employed to determine the stability and bifurcation of the periodic responses of the gear transmission system. Some interesting phenomena exist in the periodic responses consisting of “softening-spring” behaviors, jump phenomena, primary resonances (PRs), and super-harmonic resonances (SP-HRs), and saddle-node bifurcations are observed. Especially, effects of loads on unstable regions, amplitudes, and positions of bifurcation points of frequency response curves are revealed. Analytical results obtained by the IHB method match very well with those from numerical integration. Full article

(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements)

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25 pages, 43246 KiB

Open AccessArticle

Benchmark Feature Detection Method for Mobile Robot Automatic Drilling System Integrated with Deep Learning

by Jialong Dai, Jianxin Shen, Wei Tian, Pengcheng Li, He Liu and Xiangshun Cui

Machines 2025, 13(2), 154; https://doi.org/10.3390/machines13020154 - 17 Feb 2025

Viewed by 385

Abstract

Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, [...] Read more.

Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, this paper proposes a method that uses a 2D industrial camera for image capture, applies deep learning for initial target recognition and positioning, and then determines the feature extraction location based on the initial recognition. The extracted benchmark positions are accurately fitted using an improved Huber algorithm. Experimental results demonstrate that this approach improves the benchmark feature detection recognition rate by 43.8%, center recognition accuracy by 78.26%, and overall hole processing accuracy by 54.69%. Full article

(This article belongs to the Special Issue Interactive Manipulation of Mobile Manipulators)

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16 pages, 4376 KiB

Open AccessReview

A Review of Machine Learning-Based Thermal Error Modeling Methods for CNC Machine Tools

by Sen Mu, Chunping Yu, Kunlong Lin, Caijiang Lu, Xi Wang, Tao Wang and Guoqiang Fu

Machines 2025, 13(2), 153; https://doi.org/10.3390/machines13020153 - 17 Feb 2025

Viewed by 924

Abstract

Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of thermal error modeling and its advancements, this paper [...] Read more.

Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation. To provide a comprehensive understanding of thermal error modeling and its advancements, this paper systematically reviews machine learning-based methods for thermal error compensation. Thermal error modeling is the most critical step in thermal error compensation, as it directly influences the effectiveness of the compensation due to its accuracy and robustness. With the rapid development of big data and artificial intelligence, machine learning has emerged as a powerful tool in thermal error modeling, leading to significant research progress in recent years. In this paper, an overview of the thermal error modeling methods based on deep learning that have been researched and applied in recent years is presented. Specifically, two methods for reducing thermal errors, namely, thermal error suppression and thermal error compensation, are introduced and analyzed. Second, machine learning-based thermal error modeling methods are categorized into traditional machine learning-driven and deep learning-driven approaches. The application of these two methods in thermal error modeling and compensation is reviewed and summarized in detail. By synthesizing these studies, this paper identifies key challenges and trends in machine learning-based thermal error modeling. Finally, the thermal error modeling methods discussed in this paper are summarized, and future research directions are proposed to further enhance modeling accuracy and robustness. Full article

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

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28 pages, 34209 KiB

Open AccessArticle

Autonomous Non-Communicative Navigation Assistance to the Ground Vehicle by an Aerial Vehicle

by Ashok Kumar Sivarathri and Amit Shukla

Machines 2025, 13(2), 152; https://doi.org/10.3390/machines13020152 - 17 Feb 2025

Viewed by 437

Abstract

Vision-based UAV-AGV (Unmanned Aerial Vehicle–Autonomous Ground Vehicle) systems are prominent for executing tasks in GPS (Global Positioning System)-inaccessible areas. One of the roles of the UAV is guiding the navigation of the AGV. Reactive/mapless navigation assistance to an AGV from a UAV is [...] Read more.

Vision-based UAV-AGV (Unmanned Aerial Vehicle–Autonomous Ground Vehicle) systems are prominent for executing tasks in GPS (Global Positioning System)-inaccessible areas. One of the roles of the UAV is guiding the navigation of the AGV. Reactive/mapless navigation assistance to an AGV from a UAV is well known and suitable for computationally less powerful systems. This method requires communication between both agents during navigation as per state of the art. However, communication delays and failures will cause failures in tasks, especially during outdoor missions. In the present work, we propose a mapless technique for the navigation of AGVs assisted by UAVs without communication of obstacles to AGVs. The considered scenario is that the AGV is undergoing sensor and communication module failure and is completely dependent on the UAV for its safe navigation. The goal of the UAV is to take AGV to the destination while guiding it to avoid obstacles. We exploit the autonomous tracking task between the UAV and AGV for obstacle avoidance. In particular, AGV tracking the motion of the UAV is exploited for the navigation of the AGV. YOLO (You Only Look Once) v8 has been implemented to detect the drone by AGV camera. The sliding mode control method is implemented for the tracking motion of the AGV and obstacle avoidance control. The job of the UAV is to localize obstacles in the image plane and guide the AGV without communicating with it. Experimental results are presented to validate the proposed method. This proves to be a significant technique for the safe navigation of the AGV when it is non-communicating and experiencing sudden sensor failure. Full article

(This article belongs to the Special Issue Guidance, Navigation and Control of Mobile Robots)

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17 pages, 5829 KiB

Open AccessArticle

Research on Remote Operation and Maintenance Based on Digital Twin Technology

by Peilu Sun and Xin Liu

Machines 2025, 13(2), 151; https://doi.org/10.3390/machines13020151 - 16 Feb 2025

Viewed by 787

Abstract

With the wide application of a new generation of information technology, remote technical services are receiving an increasing amount of attention in the manufacturing field. In view of the fact that most mechanical and electrical equipment manufacturing enterprises still need to send a [...] Read more.

With the wide application of a new generation of information technology, remote technical services are receiving an increasing amount of attention in the manufacturing field. In view of the fact that most mechanical and electrical equipment manufacturing enterprises still need to send a substantial number of employees to the site to provide operation and maintenance services for customers, and the operation and maintenance costs of enterprises remain high, a remote operation and maintenance method of mechanical and electrical equipment based on digital twin technology is proposed. A digital twin remote operation and maintenance services model is constructed, and digital twin remote operation and maintenance technology is divided into four basic levels: virtual simulation, software/hardware-in-the-loop virtual commissioning, virtual and real synchronization, and cloud–end interconnection. With this, we conduct in-depth research on the key technologies involved in these four levels. The digital twin remote operation and maintenance service platform has been built; this platform can provide maintenance, repair, overhaul, and operation services for enterprise equipment. The feasibility of both the digital twin remote operation and maintenance services model and the digital twin remote operation and maintenance service platform was verified through cases, which provided an efficient and feasible solution for enterprises to improve service efficiency and reduce labor costs. Full article

(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)

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12 pages, 955 KiB

Open AccessArticle

Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives

by Manuel G. Satué, Manuel R. Arahal and Manuel G. Ortega

Machines 2025, 13(2), 150; https://doi.org/10.3390/machines13020150 - 15 Feb 2025

Viewed by 407

Abstract

Variable speed drives are often controlled by a double-loop scheme in which a proportional integral controller takes on the speed loop. The tuning of this loop is a complex job. In most cases just mechanical variables are considered for tuning. This paper presents [...] Read more.

Variable speed drives are often controlled by a double-loop scheme in which a proportional integral controller takes on the speed loop. The tuning of this loop is a complex job. In most cases just mechanical variables are considered for tuning. This paper presents a new Pareto analysis incorporating mechanical and electrical variables. A state of the art finite state model predictive controller is used for stator current control. The analysis is performed using experimental data from a five-phase induction motor and considers considering commonly found performance indicators derived from experimental data. The results show undocumented connections between those performance indicators. The analysis not only helps in PI tuning but, more importantly, prompts for a revision of the methods usually utilized to report performance enhancements of new methods. Full article

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

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15 pages, 5137 KiB

Open AccessArticle

The Design and Research of a Cup Body Weld Seam Polishing Positioning System Based on Machine Vision

by Yanqing Qiu, Zhinan Zhao, Tingting Lang and Wei Liu

Machines 2025, 13(2), 149; https://doi.org/10.3390/machines13020149 - 14 Feb 2025

Viewed by 386

Abstract

A machine vision-based weld seam positioning system for thermos cups is proposed. The system’s operational flow and image algorithms are designed to achieve automatic calibration of the weld seam orientation. Experimental results show a qualification rate of 97.8%, demonstrating robust performance across various [...] Read more.

A machine vision-based weld seam positioning system for thermos cups is proposed. The system’s operational flow and image algorithms are designed to achieve automatic calibration of the weld seam orientation. Experimental results show a qualification rate of 97.8%, demonstrating robust performance across various cup models. The average positioning error is 45 pixels, with a maximum error of 153 pixels, corresponding to angular deviations of 0.68° and 2.30°, respectively. The average processing time of a single algorithm run is 128 ms, ensuring efficient operation in non-high-speed production scenarios. The results of this study have good application value and also provide some insights for the position calibration of other rotational objects. Full article

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

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20 pages, 11911 KiB

Open AccessArticle

Comparison of Automation-Supported and Conventional Methods for Measuring Energy Consumption in Computer Numerical Control Machining

by Erman Zurnacı, Sabri Uzuner and Engin Nas

Machines 2025, 13(2), 148; https://doi.org/10.3390/machines13020148 - 14 Feb 2025

Viewed by 742

Abstract

Optimizing energy consumption in machining processes is critical for achieving sustainable manufacturing. This study introduces an Automation-Supported measurement approach that integrates a custom power analyzer with real-time data logging and visualization capabilities to accurately measure energy usage during CNC (computer numerical control) operations. [...] Read more.

Optimizing energy consumption in machining processes is critical for achieving sustainable manufacturing. This study introduces an Automation-Supported measurement approach that integrates a custom power analyzer with real-time data logging and visualization capabilities to accurately measure energy usage during CNC (computer numerical control) operations. Statistical comparisons were conducted using the independent samples t-test and Taguchi analysis to evaluate the effectiveness of the proposed method against traditional measurement techniques. The results revealed that there is a statistically significant difference (p < 0.05) in the current measurements across X, Z, and spindle motors between the proposed and conventional methods. The advanced method based on automation reduced the error rate in measuring spindle motor power consumption due to the selection of processing parameters from 34.17% to 2.7%. Additionally, Taguchi analysis demonstrated that the measurement method influenced the optimization of machining parameters, with S/N ratio improvements observed. These findings confirm that the proposed method enhances energy efficiency, reduces environmental impact, and supports sustainable manufacturing practices. Full article

(This article belongs to the Section Industrial Systems)

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20 pages, 8828 KiB

Open AccessArticle

Comparative Study of Hydrodynamic Performance of Submerged Water Jet Propeller and Conventional Propeller Under Multiple Operating Conditions

by Jiayi Li, Lei Ma, Dongyang Chen, Yunpeng Qi, Tiechao Bai and Guang Pan

Machines 2025, 13(2), 147; https://doi.org/10.3390/machines13020147 - 13 Feb 2025

Viewed by 588

Abstract

As global shipping accelerates toward a green and low-carbon transformation, submerged water jet propulsion has emerged as a promising alternative to traditional propellers due to its high speed efficiency, noise reduction, and adaptability. This study establishes a high-fidelity CFD (computational fluid dynamics) model [...] Read more.

As global shipping accelerates toward a green and low-carbon transformation, submerged water jet propulsion has emerged as a promising alternative to traditional propellers due to its high speed efficiency, noise reduction, and adaptability. This study establishes a high-fidelity CFD (computational fluid dynamics) model incorporating vehicle body wake characteristics, validated through open-water experiments. A comparative analysis reveals that the vehicle body wake improves propulsion efficiency by 4.66% for conventional propellers and 2.32% for submerged water jet systems in near-surface operations while exacerbating cavitation-induced efficiency losses by 1.7% and 1.0%, respectively. Notably, submerged water jet propulsion demonstrates superior performance under high-velocity conditions, achieving 5–12.27% higher efficiency than conventional propellers across both open-water and vehicle body wake-affected scenarios. These findings substantiate submerged water jet propulsion’s advantages in complex flow fields, offering critical insights for marine propulsion system optimization. Full article

(This article belongs to the Special Issue Advancements in Fluid-Structure Interaction Vibration Control Technology)

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15 pages, 10230 KiB

Open AccessArticle

A Real-Time Demanded Current Observation-Based Sliding Mode Control of Permanent Magnet Synchronous Motors

by Guangping Li, Guolong Zhong, Likun Hu, Pingping Gong and Gaoxiang Li

Machines 2025, 13(2), 146; https://doi.org/10.3390/machines13020146 - 13 Feb 2025

Viewed by 411

Abstract

To improve the dynamic and steady-state performance of permanent magnet synchronous motors (PMSM), this paper proposes a real-time demanded current observation based sliding mode control strategy. Firstly, based on the mechanism between motor speed and current, a real-time demanded current observer is proposed, [...] Read more.

To improve the dynamic and steady-state performance of permanent magnet synchronous motors (PMSM), this paper proposes a real-time demanded current observation based sliding mode control strategy. Firstly, based on the mechanism between motor speed and current, a real-time demanded current observer is proposed, which can obtain the demanded current based on the motor speed. Secondly, the demanded current is directly used to calibrate the q-axis current reference in real-time, which can update the current reference value faster than the sliding mode controller. Due to the real-time demanded current feedforward, the anti-interference performance of the PMSM under the proposed control is improved effectively. Compared with the conventional sliding mode control, the proposed control has a better dynamic and steady-state performance. Finally, the effectiveness of the proposed control has been verified through simulation and experiments. Full article

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

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33 pages, 6763 KiB

Open AccessArticle

Modified Dynamic Movement Primitive-Based Closed Ankle Reduction Technique Learning and Variable Impedance Control for a Redundant Parallel Bone-Setting Robot

by Zhao Tan, Yahui Zhang, Jiahui Yuan, Xu Song, Jialong Zhang, Guilin Wen, Xiaoyan Hu and Hanfeng Yin

Machines 2025, 13(2), 145; https://doi.org/10.3390/machines13020145 - 13 Feb 2025

Cited by 1 | Viewed by 531

Abstract

Traditional fracture reduction relies heavily on the surgeon’s experience, which hinders the transmission of skills. This specialization bottleneck, coupled with the high demands on physical strength, significantly limits the efficiency of daily treatments in trauma orthopedics. Currently, most fracture surgery robots focus on [...] Read more.

Traditional fracture reduction relies heavily on the surgeon’s experience, which hinders the transmission of skills. This specialization bottleneck, coupled with the high demands on physical strength, significantly limits the efficiency of daily treatments in trauma orthopedics. Currently, most fracture surgery robots focus on open or minimally invasive reduction techniques, which inherently carry the risk of iatrogenic damage due to surgical incisions or bone pin insertions. However, research in closed reduction-oriented robotic systems is remarkably limited. Addressing this gap, our study introduces a novel bone-setting robot for the closed reduction of ankle fractures designed with a redundant parallel platform. The parallel robot’s design incorporates three sliding redundancy actuators that enhance its tilt flexibility while maintaining load performance. Moreover, a singularity-free redundant kinematic solver has been developed, optimizing the robot’s operational efficacy. Building upon the demonstrations from professional closed reduction techniques, we propose the use of a multivariate Student-t process as a multi-output regression model within dynamic movement primitive for accurately learning stable reduction maneuvers. Additionally, we develop an anthropomorphic variable impedance controller based on inverse dynamics. The simulation results demonstrate convincingly that the developed ankle bone-setting robot is proficient in effectively replicating and learning the nuanced closed reduction techniques. Full article

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

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16 pages, 676 KiB

Open AccessReview

Condition Monitoring of Electric Machines: Modern Frameworks and Data-Driven Methodologies

by Wesley Doorsamy

Machines 2025, 13(2), 144; https://doi.org/10.3390/machines13020144 - 13 Feb 2025

Viewed by 1219

Abstract

Electrical machines are at the centre of most engineering processes, with rotating electrical machines, in particular, becoming increasingly important in recent history due to their growing applications in electric vehicles and renewable energy. Although the landscape of condition monitoring in electrical machines has [...] Read more.

Electrical machines are at the centre of most engineering processes, with rotating electrical machines, in particular, becoming increasingly important in recent history due to their growing applications in electric vehicles and renewable energy. Although the landscape of condition monitoring in electrical machines has evolved over the past 50 years, the intensification of engineering efforts towards sustainability, reliability, and efficiency, coupled with breakthroughs in computing, has prompted a data-driven paradigm shift. This paper explores the evolution of condition monitoring of rotating electrical machines in the context of maintenance strategy, focusing on the emergence of this data-driven paradigm. Due to the broad and varying nature of condition monitoring practices, a framework is also offered here, along with other essential terms of reference, to provide a concise overview of recent developments and to highlight the modern challenges and opportunities within this area. The paper is purposefully written as a tutorial-style overview for the benefit of practising engineers and researchers who are new to the field or not familiar with the wider intricacies of modern condition monitoring systems. Full article

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

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17 pages, 7886 KiB

Open AccessArticle

Multi-Objective Lightweight Design Method for Lower Control Arms Based on Multi-Axial Random Vibration Fatigue Life

by Yan Zhang, Qi Li, Jie Wan and Haodong Sun

Machines 2025, 13(2), 143; https://doi.org/10.3390/machines13020143 - 13 Feb 2025

Viewed by 616

Abstract

To reduce the computational cost of the multi-objective optimization process and improve the accuracy of fatigue life prediction for the lower control arm (LCA) of a vehicle under multiaxial random vibration, this paper focuses on the LCA of a MacPherson suspension in a [...] Read more.

To reduce the computational cost of the multi-objective optimization process and improve the accuracy of fatigue life prediction for the lower control arm (LCA) of a vehicle under multiaxial random vibration, this paper focuses on the LCA of a MacPherson suspension in a specific vehicle model and proposes a multi-objective lightweight design method based on multiaxial random vibration fatigue. This method combines the Latin hypercube sampling (LHS) technique, Kriging surrogate modeling, and the second-generation non-dominated sorting genetic algorithm (NSGA-II). First, static and dynamic analyses are conducted to extract the design parameters required to meet the design specifications of the reference LCA. Subsequently, the LHS technique is employed to obtain 50 sample points, which are used to construct the sample space. The Kriging method is then applied to build surrogate models that capture the relationship between design variables and various responses. Finally, the NSGA-II multi-objective genetic algorithm is utilized to obtain the optimized solution. Considering 1.2 times the safe driving distance, the optimal solution with the minimum LCA mass is selected from the Pareto frontier. The optimization results show that compared to the initial LCA model, the mass of the optimized model is reduced by 13.06%, the fatigue life is increased by 47.48%, and the maximum displacement and maximum stress are reduced by 1.78% and 4.31%, respectively. Additionally, the first-order modal frequency decreases by 4.60%. Full article

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

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14 pages, 11249 KiB

Open AccessArticle

Loaded Surface Shape Stability Analysis of Short-Focus Off-Axis Aspheres for Stressed Mirror Polishing

by Jinfeng Li, Haifei Hu, Junqing Zhu, Han Pei and Le Liu

Machines 2025, 13(2), 142; https://doi.org/10.3390/machines13020142 - 13 Feb 2025

Viewed by 479

Abstract

The Stressed Mirror Polishing method (SMP) enables the high-quality and efficient fabrication of short-focus off-axis aspheres (SFOA) for overcoming difficulties arising from large asphericity, which makes the polishing tool unfitted for the mirror surface shape and produces mid- and high-frequency errors. However, there [...] Read more.

The Stressed Mirror Polishing method (SMP) enables the high-quality and efficient fabrication of short-focus off-axis aspheres (SFOA) for overcoming difficulties arising from large asphericity, which makes the polishing tool unfitted for the mirror surface shape and produces mid- and high-frequency errors. However, there remains a lack of precise iterative loading methods and detailed analyses of loaded surface shape stability for processing SFOA through SMP. Therefore, a foundational analysis model for SMP was established, integrating mathematical theoretical modeling with a finite element analysis (FEA). Based on this model, an iterative loading strategy, based on the proportional relationship between the Zernike coefficients and the SMP-type surface shape error, was proposed to achieve a high dynamic range of >100, which is 10 times larger than the reported ones. Moreover, to guide the loading structure design and quantify the surface shape’s sensitivity to design parameters, both design of experiments (DOE) and radial basis function (RBF) methods had been used to ascertain the precision requirement of each force and the corresponding arm length at separated loading points, under the shape precision requirement of PV < 1 μm which coordinates with the ability of the Ion Beam Figuring (IBF) technique. Full article

(This article belongs to the Section Advanced Manufacturing)

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15 pages, 4002 KiB

Open AccessArticle

Condition-Based Maintenance for Degradation-Aware Control Systems in Continuous Manufacturing

by Faisal Alsaedi and Sara Masoud

Machines 2025, 13(2), 141; https://doi.org/10.3390/machines13020141 - 12 Feb 2025

Viewed by 762

Abstract

To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we [...] Read more.

To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we propose a framework explicitly tailored for degradation-aware control systems, built upon two main components: (1) degradation modeling to estimate and track hidden degradation over time and (2) a Long Short-Term Memory Autoencoder-Degradation Stage Detector (A-LSTMA-DSD) to define alarm and failure thresholds for enabling condition-based maintenance. In degradation modeling, the framework utilizes actuator measurements to model hidden degradation. Next, an A-LSTMA-DSD model is developed to flag anomalies, based on which alarm and failure thresholds are assigned. These dynamic thresholds are defined to ensure sufficient time for addressing maintenance requirements. Working with real data from a boiler unit in an oil refinery and focusing on steam leakages, our proposed framework successfully identified all failures and on average triggered alarm and failure thresholds 15 and 8 days in advance of failures, respectively. In addition to triggering these thresholds, our system outperforms baseline models, such as CNN, LSTM, ANN, ARIMA, and Facebook Profit, in identifying failures by 60% and 95%, respectively. Full article

(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)

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57 pages, 12798 KiB

Open AccessReview

Advances in Computer Numerical Control Geometric Error Compensation: Integrating AI and On-Machine Technologies for Ultra-Precision Manufacturing

by Yassmin Seid Ahmed and Fred Lacerda Amorim

Machines 2025, 13(2), 140; https://doi.org/10.3390/machines13020140 - 12 Feb 2025

Cited by 1 | Viewed by 1028

Abstract

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant [...] Read more.

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques. Full article

(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)

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21 pages, 10018 KiB

Open AccessArticle

Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces

by Luca Radicioni, Francesco Morgan Bono and Simone Cinquemani

Machines 2025, 13(2), 139; https://doi.org/10.3390/machines13020139 - 12 Feb 2025

Cited by 1 | Viewed by 972

Abstract

In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This [...] Read more.

In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. By transforming vibration signals into images using the Synchrosqueezing Transform (SST), this research leverages the strengths of convolutional neural networks (CNNs) in image processing, which have proven effective in AD, especially at the pixel level. The methodology involves training CAEs and VAEs on data from machinery in healthy condition and testing them on new data samples representing different levels of system degradation. The results indicate that models with spatial latent spaces outperform those with dense latent spaces in terms of reconstruction accuracy and AD capabilities. However, VAEs did not yield satisfactory results, likely because reconstruction-based metrics are not entirely useful for AD purposes in such models. This study also highlights the potential of ReLU residuals in enhancing the visibility of anomalies. The data used in this study are openly available. Full article

(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)

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