Machines

16 pages, 4191 KiB

Open AccessArticle

Digital Twins for Defect Detection in FDM 3D Printing Process

by Chao Xu, Shengbin Lu, Yulin Zhang, Lu Zhang, Zhengyi Song, Huili Liu, Qingping Liu and Luquan Ren

Machines 2025, 13(6), 448; https://doi.org/10.3390/machines13060448 (registering DOI) - 23 May 2025

Abstract

Additive manufacturing (AM, also known as 3D printing) is a bottom–up process where variations in process conditions can significantly influence the quality and performance of the printed parts. Digital twin (DT) technology can measure process parameters and printed part characteristics in real-time, achieving [...] Read more.

Additive manufacturing (AM, also known as 3D printing) is a bottom–up process where variations in process conditions can significantly influence the quality and performance of the printed parts. Digital twin (DT) technology can measure process parameters and printed part characteristics in real-time, achieving online monitoring, analysis, and optimization of the AM process. Existing DT research on AM focuses on simulating the printing process and lacks real-time defect detection and twinning of actual printed objects, which hinders the timely detection and correction of defects. This study developed a DT system for fused deposition modeling (FDM) AM technology that not only accurately simulates the printing process but also performs real-time quality monitoring of the printed parts. A laser profilometer and industrial camera were integrated into the printer to detect and collect real-time morphological data on the printed object. The custom-developed DT software could convert the morphological data of the printed parts into a DT model. By comparing the DT model of the printed object with its three-dimensional model, defect detection of the printed parts was achieved, where the quality of the printed parts was evaluated using a defect percentage index. This study combines DT and AM to achieve process quality monitoring, demonstrating the potential of DT technology in reducing printing defects and improving the quality of printed parts. Full article

(This article belongs to the Section Advanced Manufacturing)

24 pages, 3888 KiB

Open AccessArticle

Fault Detection in Gearboxes Using Fisher Criterion and Adaptive Neuro-Fuzzy Inference

by Houssem Habbouche, Tarak Benkedjouh, Yassine Amirat and Mohamed Benbouzid

Machines 2025, 13(6), 447; https://doi.org/10.3390/machines13060447 (registering DOI) - 23 May 2025

Abstract

Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying [...] Read more.

Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying expert methods for fault detection and diagnosis is crucial to ensuring the reliability and efficiency of these systems. Artificial intelligence (AI) techniques show promise for fault diagnosis, but their accuracy can be hindered by noise and manufacturing imperfections that distort mechanical signatures. Thorough data analysis and preprocessing are vital to preserving these critical features. Validating approaches through numerical simulations before experimentation is essential to identify model limitations and minimize risks. A hybrid approach, combining AI and physics-based models, could provide a robust solution by leveraging the strengths of both domains: AI for its ability to process large volumes of data and physics-based models for their reliability in modeling complex mechanical behaviors. This paper proposes a comprehensive diagnostic methodology. It starts with feature extraction from time-domain analysis, which helps identify critical indicators of gearbox performance. Following this, a feature selection process is applied using the Fisher criterion, which ensures that only the most relevant features are retained for further analysis. These selected features are then employed to train an Adaptive Neuro-Fuzzy Inference System (ANFIS), a sophisticated approach that combines the learning capabilities of neural networks with the reasoning abilities of fuzzy logic. The proposed methodology is evaluated using a dataset of gear faults generated through energy simulations based on a six-degree-of-freedom (6-DOF) model, followed by a secondary validation on an experimental dataset. Full article

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

17 pages, 5612 KiB

Open AccessArticle

A 3-D Defect Profile Inversion Method Based on the Continuity Correction Strategy

by Chenlin Wang, Jianhua Tang, Yicheng Duan, Senxiang Lu and Hao Wen

Machines 2025, 13(6), 446; https://doi.org/10.3390/machines13060446 - 23 May 2025

Abstract

Magnetic flux leakage (MFL) detection is widely used in the non-destructive testing of pipelines. Inversion is a key step in MFL detection, and iterative inversion based on an optimization algorithm is an effective method for constructing the 3-D profile of defects in pipelines. [...] Read more.

Magnetic flux leakage (MFL) detection is widely used in the non-destructive testing of pipelines. Inversion is a key step in MFL detection, and iterative inversion based on an optimization algorithm is an effective method for constructing the 3-D profile of defects in pipelines. However, conventional inversion algorithms suffer from weak convergence ability and are prone to local optima, which can lead to significant errors in the results. To improve the algorithm’s convergence and result accuracy, this paper proposes a “continuity correction” strategy based on geometric features of defects, which examines the relative depth value of each depth point and corrects the values of implausible points. On this basis, this paper combines the secretary bird optimization algorithm (SBOA) with the differential evolution strategy and the perturbation strategy to enhance the search capability. Through an accuracy experiment and a robustness experiment on an experimental platform, it is demonstrated that the method outperforms conventional algorithms in terms of accuracy and robustness against interference when constructing the 3-D profile of defects. Full article

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

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28 pages, 3769 KiB

Open AccessArticle

Multisensor Fault Diagnosis of Rolling Bearing with Noisy Unbalanced Data via Intuitionistic Fuzzy Weighted Least Squares Twin Support Higher-Order Tensor Machine

by Shengli Dong, Yifang Zhang and Shengzheng Wang

Machines 2025, 13(6), 445; https://doi.org/10.3390/machines13060445 - 22 May 2025

Abstract

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability [...] Read more.

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability. Full article

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

22 pages, 7227 KiB

Open AccessArticle

Analytical and Experimental Investigation of Nonlinear Dynamic Characteristics of Hydrodynamic Bearings for Oil Film Instability Detection

by Yang Chen, Zequn Zhao, Hao Zhang, Xin Li and Zhanqun Shi

Machines 2025, 13(6), 444; https://doi.org/10.3390/machines13060444 - 22 May 2025

Abstract

Nonlinear vibration phenomena, such as oil whirl and oil whip, are common indicators of oil film instability in hydrodynamic bearings and are key signs of potential faults in rotating machinery. Excessive vibrations caused by oil film instability can accelerate bearing wear and lead [...] Read more.

Nonlinear vibration phenomena, such as oil whirl and oil whip, are common indicators of oil film instability in hydrodynamic bearings and are key signs of potential faults in rotating machinery. Excessive vibrations caused by oil film instability can accelerate bearing wear and lead to the failure of the rotating system. This paper presents a model for nonlinear dynamic coefficients, aimed at providing a quantitative approach for monitoring and predicting oil film instability. The impact of operational parameters and perturbation values on both linear and nonlinear stiffness and damping coefficients is investigated. Simulation results and experimental rotor vibration signals demonstrate that the nonlinear dynamic coefficient model effectively characterizes oil film instability and accurately predicts rotor trajectory, while traditional linear models are only applicable under low-speed and small-disturbance conditions. Compared to traditional analytical models and numerical solutions, the nonlinear dynamic coefficients have higher accuracy and efficiency and can reliably identify the onset frequency of oil film instability. This study clarifies the relationship between nonlinear dynamic coefficients and rotor dynamic response, laying a theoretical foundation for the monitoring and prediction of oil film instability. Full article

(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)

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22 pages, 370 KiB

Open AccessArticle

Extended Dissipativity Analysis for Uncertain Neutral-Type Semi-Markovian Jump Systems via Two Integral Inequalities

by Zihao Gao and Huaguang Zhang

Machines 2025, 13(6), 443; https://doi.org/10.3390/machines13060443 - 22 May 2025

Abstract

This paper addresses the problem of extended dissipativity analysis for uncertain neutral-type semi-Markovian jump systems. Two novel parameter-dependent, free-matrix-based integral inequalities are proposed by introducing some adjustable parameters, from which some existing integral inequalities can be covered, such as traditional free-matrix-based integral inequalities [...] Read more.

This paper addresses the problem of extended dissipativity analysis for uncertain neutral-type semi-Markovian jump systems. Two novel parameter-dependent, free-matrix-based integral inequalities are proposed by introducing some adjustable parameters, from which some existing integral inequalities can be covered, such as traditional free-matrix-based integral inequalities and Wirtinger-based integral inequalities. A significant advancement lies in the incomplete slack matrices, with some zero components in these inequalities removed, leading to fully coupled system information. An innovative condition for extended dissipativity is derived, specifically tailored to the systems under investigation and based on the newly formulated inequalities. To demonstrate the efficacy and superiority of the methodologies, two numerical examples are meticulously provided. Full article

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

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37 pages, 2396 KiB

Open AccessReview

A Review of Hierarchical Control Strategies for Lower-Limb Exoskeletons in Children with Cerebral Palsy

by Ziwei Kang, Hui Li, Yang Wang and Hongliu Yu

Machines 2025, 13(6), 442; https://doi.org/10.3390/machines13060442 - 22 May 2025

Abstract

In recent years, with the deepening research on exoskeletons for children with cerebral palsy, increasing evidence has highlighted their unique characteristics. Unlike adult exoskeletons, pediatric exoskeletons cannot be simply realized by scaling down adult designs; instead, special attention must be given to their [...] Read more.

In recent years, with the deepening research on exoskeletons for children with cerebral palsy, increasing evidence has highlighted their unique characteristics. Unlike adult exoskeletons, pediatric exoskeletons cannot be simply realized by scaling down adult designs; instead, special attention must be given to their unique training requirements. Although current studies have incorporated specific design adaptations and summarized the distinct features of these devices, a comprehensive review of control strategies remains lacking. This study adopts a structured narrative review approach, referencing the PRISMA framework to enhance transparency in the literature selection. Relevant publications were identified based on clearly defined inclusion and exclusion criteria, but no formal systematic review or meta-analysis was conducted. The exoskeleton control strategies from the 106 selected articles are classified using a hierarchical framework, dividing them into the supervision layer, action layer, and execution layer, with a further categorization into 12 specific control methods. Findings indicate that the supervision level primarily employs finite state machines and linear phase estimation, while the action level predominantly utilizes position trajectory control, torque trajectory control, and impedance control. At the execution level, closed-loop torque control and position control are commonly adopted. Overall, existing studies still face challenges in personalized adaptation, real-time control, and application scenarios. With advancements in controller hardware and the introduction of novel actuators, emerging technologies such as machine learning, virtual constraints, and sliding mode control may offer promising directions for future pediatric exoskeleton control design. Full article

(This article belongs to the Special Issue Advances in Medical and Rehabilitation Robots)

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22 pages, 4392 KiB

Open AccessArticle

A Study on the Establishment of a Variable Stiffness Physical Model of Abdominal Soft Tissue and an Interactive Massage Force Prediction Algorithm

by Xinyi Tang, Ping Shi, Zhenjie Luo, Sujiao Li and Hongliu Yu

Machines 2025, 13(6), 441; https://doi.org/10.3390/machines13060441 - 22 May 2025

Abstract

This research focuses on the crucial issue of ascertaining safe and suitable massage forces for abdominal massage robots. It does so by constructing variable stiffness soft tissue physical models and devising a machine learning-based prediction algorithm. Twelve healthy volunteers with diverse body types [...] Read more.

This research focuses on the crucial issue of ascertaining safe and suitable massage forces for abdominal massage robots. It does so by constructing variable stiffness soft tissue physical models and devising a machine learning-based prediction algorithm. Twelve healthy volunteers with diverse body types (underweight, standard, overweight) were enlisted. A self-developed experimental platform was utilized to gather high-precision mechanical data. Two physical models were formulated to depict the nonlinear force-displacement relationship of abdominal soft tissues. Model 1 was based on an exponential function, and Model 2 was based on a power function. Model 1 showed greater accuracy for standard body types (R² > 0.95 at 72.22% of data points) and underweight body types (R² > 0.95 at 100% of data points). In contrast, Model 2 was a better fit for overweight individuals (R² > 0.95 at 47.06% of data points). Furthermore, a transformer-based machine learning algorithm was developed. This algorithm predicts interaction forces using anthropometric and physiological characteristics. It achieved an accuracy of 92.60% within a pressing depth of 0–15 mm. In most situations, especially in full-range predictions (with an accuracy of 58.60%), the algorithm outperformed both physical models. This study presents a dual framework that combines biomechanical modeling and data-driven algorithms. It lays the theoretical and practical groundwork for safe and adaptable force control in abdominal massage robotics. Full article

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

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18 pages, 4153 KiB

Open AccessArticle

Analysis of Electromagnetic Characteristics of Outer Rotor Type BLDC Motor Based on Analytical Method and Optimal Design Using NSGA-II

by Tae-Seong Kim, Jun-Won Yang, Kyung-Hun Shin, Gang-Hyeon Jang, Cheol Han and Jang-Young Choi

Machines 2025, 13(6), 440; https://doi.org/10.3390/machines13060440 - 22 May 2025

Abstract

This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study [...] Read more.

This study investigates the electromagnetic analysis and optimal design of outer rotor type brushless DC (BLDC) motors for fan filter applications. The primary objective is to develop a method that integrates three-dimensional (3D) structural effects with efficient two-dimensional (2D) equivalent analysis. This study proposes a 2D equivalent analysis method that addresses the unique features of outer rotor type BLDC motors, particularly the permanent magnet (PM) overhang structure. This approach transforms the operating point on the B–H curve to facilitate accurate modeling in a 2D framework, overcoming traditional analysis limitations. An analytical method using spatial harmonics is introduced to derive essential electromagnetic quantities, namely flux linkage and back electromotive force (EMF). The method compensates for slot effects using the Carter coefficient, ensuring precise evaluation of circuit parameters and electromagnetic losses. To optimize motor performance, a multi-objective optimization technique is implemented using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), aiming to maximize both efficiency and power density. The research validates the proposed analytical approach against the finite element analysis method (FEM) results to confirm its accuracy. Full article

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

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18 pages, 4228 KiB

Open AccessArticle

Transition Process Control of Tiltrotor Aircraft Based on Fractional-Order Model Reference Adaptive Control

by Junkai Liang, Hui Ye, Yaohua Shen and Dawei Wu

Machines 2025, 13(6), 439; https://doi.org/10.3390/machines13060439 - 22 May 2025

Abstract

To address the critical challenge of controlling tiltrotor aircraft during transition mode, this paper proposes a fractional-order model reference adaptive control (FO-MRAC) method based on the unique modeling of the tiltrotor aircraft. A nonlinear model capturing the dynamic characteristics of the tiltrotor aircraft [...] Read more.

To address the critical challenge of controlling tiltrotor aircraft during transition mode, this paper proposes a fractional-order model reference adaptive control (FO-MRAC) method based on the unique modeling of the tiltrotor aircraft. A nonlinear model capturing the dynamic characteristics of the tiltrotor aircraft during the transition mode is developed based on an accurate analysis of the forces and moments acting on key components. This model is subsequently linearized to obtain a stable flight envelope. Considering the complexity of transition, the FO-MRAC method is designed based on the shift of the equilibrium point for superior parameter tuning and disturbance rejection. Then, the stability of the closed-loop system is analyzed using the Lyapunov stability theory. Finally, an experimental platform is constructed to verify the validity of the aerodynamic modeling and the designed control method. Full article

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

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21 pages, 6403 KiB

Open AccessArticle

Autonomous Sewer Robot: A Laser Marker-Based Detection System

by Vygantas Ušinskis, Andrius Dzedzickis, Justas Nekrašas and Vytautas Bučinskas

Machines 2025, 13(5), 438; https://doi.org/10.3390/machines13050438 - 21 May 2025

Abstract

Navigation technologies are becoming more advanced, helping to solve complicated problems in various fields. Navigation can be classified as global, in which predefined data and reference points from the working environment are used to generate a path, and local, where the map is [...] Read more.

Navigation technologies are becoming more advanced, helping to solve complicated problems in various fields. Navigation can be classified as global, in which predefined data and reference points from the working environment are used to generate a path, and local, where the map is generated momentarily by acquiring data from outside using sensors. As navigation tasks become more demanding, working environments can become very complicated with an increasing number of dynamic obstacles or, in some cases, a lack of global references, which may have particularly notable impacts on communication. Inspection robots that are required to work in underground sewers are often required to work completely locally, relying on sensor data. For this reason, in this study, a cost-efficient laser marker-based obstacle detection and measurement system is designed and tested for future use in autonomous sewer robot local path generation. Our experiments show the convenience of applying four linear laser markers with an RGB camera to fully inspect upcoming obstacles in the region covering the front robot dimensions. The results show distance measurement accuracy of up to ±1.33 mm and obstacle width accuracy of up to ±0.28 mm measured from a 130 mm range. Nevertheless, accuracy is strongly dependent on the segmentation of image resolution, lighting, and reflection of the inspected surfaces. Also, it depends on the configuration of laser markers and the RGB camera position. Full article

(This article belongs to the Special Issue Mechatronic Systems: Developments and Applications)

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27 pages, 8009 KiB

Open AccessArticle

Electromagnetic–Mechanical–Acoustic Coupling Analysis of Transformers Under Geomagnetically Induced Current Interference

by Jingge An, Chao Pan and Xiaobo Shi

Machines 2025, 13(5), 437; https://doi.org/10.3390/machines13050437 - 21 May 2025

Abstract

During geomagnetic storms, a geomagnetically induced current (GIC) flows into grounding transformers, potentially causing anomalous vibrations and audible noise in internal components. This study establishes an electromagnetic–mechanical–acoustic coupling (EMAC) model to characterize the multi-physics interactions in transformers under GIC interference. Based on the [...] Read more.

During geomagnetic storms, a geomagnetically induced current (GIC) flows into grounding transformers, potentially causing anomalous vibrations and audible noise in internal components. This study establishes an electromagnetic–mechanical–acoustic coupling (EMAC) model to characterize the multi-physics interactions in transformers under GIC interference. Based on the measured data, the GIC is classified into fluctuating and constant components according to its fluctuation characteristics. A propagation-path-based coupling model is proposed to investigate the correlated interactions among physical fields, extracting critical parameters, including winding current, magnetic flux, electromagnetic force, vibration, and noise. Comparative simulations reveal that the fluctuating component induces more complex multi-physics variations, generating significantly higher vibration amplitudes and noise levels compared to those of the constant component. A dynamic experimental platform is built to obtain multi-physics field information in different modes, and the effectiveness of the model and the correctness of the conclusions are verified through virtual–physical consistency validation. On this basis, multimodal feature information domains are established to delineate the operational state intervals of the transformer under GIC interference. Stability threshold criteria are subsequently developed, providing a critical quantitative basis for the condition monitoring of power transformers. Full article

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

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24 pages, 3679 KiB

Open AccessArticle

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

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 KiB

Open AccessArticle

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

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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18 pages, 4863 KiB

Open AccessArticle

Fault Diagnosis in a 2 MW Wind Turbine Drive Train by Vibration Analysis: A Case Study

by Rafael Tuirán, Héctor Águila, Esteve Jou, Xavier Escaler and Toufik Mebarki

Machines 2025, 13(5), 434; https://doi.org/10.3390/machines13050434 - 20 May 2025

Abstract

This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary [...] Read more.

This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary stage and two helical gear stages. Failures in two pairs of helical gears were identified: one involving pitting and wear in the gears connecting the intermediate-speed shaft to the low-speed shaft, and another one involving significant material detachment in the gears connecting the intermediate-speed shaft to the high-speed shaft. The continuous evaluation of time signals, frequency spectra, and amplitude modulations allowed the most sensitive sensors and frequencies for predicting surface damage on gear teeth in this type of turbine to be determined. A steady-state frequency analysis was performed, enabling the detection of the aforementioned surface faults. This approach is simpler compared with more complex transient-state techniques. By tracking vibration signals over time, the importance of analyzing gear mesh frequencies and their harmonics was highlighted. Additionally, it was found that the progression of gear damage was dependent on the power output of the wind turbine. As a result, the most appropriate ranges of power were identified, within which the evolution of the vibration measurement was associated with the damage evolution. Since many turbines currently in operation have similar designs and power output levels, the present findings can serve as a guideline for monitoring an extensive number of units. Full article

(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis, 2nd Edition)

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29 pages, 7349 KiB

Open AccessArticle

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

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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19 pages, 10794 KiB

Open AccessArticle

The Innovative Design and Performance Testing of a Mobile Robot for the Automated Installation of Spacers on Six-Split Transmission Lines

by Jie Pan, Yongfeng Cheng, Chunhua Hu, Ming Jiang, Yong Ma, Fanhao Meng and Qiang Shi

Machines 2025, 13(5), 432; https://doi.org/10.3390/machines13050432 - 19 May 2025

Abstract

The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the [...] Read more.

The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry. Full article

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

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26 pages, 7159 KiB

Open AccessArticle

Methodology for Human–Robot Collaborative Assembly Based on Human Skill Imitation and Learning

by Yixuan Zhou, Naisheng Tang, Ziyi Li and Hanlei Sun

Machines 2025, 13(5), 431; https://doi.org/10.3390/machines13050431 - 19 May 2025

Abstract

With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which [...] Read more.

With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which utilizes electromyography (EMG) signals as an interaction interface and integrates human skill imitation with reinforcement learning. Specifically, to manage the dynamic variation in muscle coordination patterns induced by joint angle changes, a temporal graph neural network enhanced with an Angle-Guided Attention mechanism is developed. This method adaptively models the topological relationships among muscle groups, enabling high-precision three-dimensional dynamic arm force estimation. Furthermore, an expert reward function and a fuzzy experience replay mechanism are introduced in the reinforcement learning model to guide the human skill learning process, thereby enhancing collaborative comfort and smoothness. The proposed approach is validated through a collaborative assembly task. Experimental results show that the proposed arm force estimation model reduces estimation errors by 10.38%, 8.33%, and 11.20% across three spatial directions compared to a conventional Deep Long Short-Term Memory (Deep-LSTM). Moreover, it significantly outperforms state-of-the-art methods, including traditional imitation learning and adaptive admittance control, in terms of collaborative comfort, smoothness, and assembly accuracy. Full article

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

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16 pages, 3243 KiB

Open AccessArticle

Comparative Analysis of Dry, Minimum Quantity Lubrication, and Nano-Reinforced Minimum Quantity Lubrication Environments on the Machining Performance of AZ91D Magnesium Alloy

by Berat Baris Buldum, Kamil Leksycki and Suleyman Cinar Cagan

Machines 2025, 13(5), 430; https://doi.org/10.3390/machines13050430 - 19 May 2025

Abstract

This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting [...] Read more.

This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article

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

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