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A Practical Methodology for Accuracy and Quality Evaluation of Structured Light Systems in Automotive Inspection
Development of an Advanced Wear Simulation Model for a Racing Slick Tire Under Dynamic Acceleration Loading
Digital Twin-Enabled Adaptive Robotics: Leveraging Large Language Models in Isaac Sim for Unstructured Environments

Journal Description

Machines

Machines is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
Journal Rank: JCR - Q2 (Engineering, Mechanical) / CiteScore - Q1 (Control and Optimization)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 2.5 (2024); 5-Year Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2075-1702

Latest Articles

21 pages, 3933 KB

Open AccessArticle

Mechanical Design and Experimental Study of a Small-Scale Wind Turbine Model

by Eduardo Muñoz-Palomeque, Segundo Esteban and Matilde Santos

Machines 2025, 13(10), 929; https://doi.org/10.3390/machines13100929 (registering DOI) - 8 Oct 2025

The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address [...] Read more.

The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address this shortcoming, this article presents the design of a low-cost, modular 3D-printed small prototype of a wind turbine. It includes a multi-hollow platform for marine environments configuration and stabilization, the turbine tower, and three blades with active pitch control, not always included in wind turbine prototypes. The modular tower design allows for easy height extensions, while the rotor incorporates custom blades optimized for the prototype geometry and experimental setup. Tests were conducted to evaluate the system’s operational response and verify the proper functioning of the assembled components at various wind speeds and blade pitch angles. The results confirm that the rotor speed with the prototype’s onshore configuration is highly pitch-dependent, reaching a maximum efficiency of approximately 5°. The tower displacement, measured with an IMU, remained within a narrow range, oscillating around 2° and reaching up to 4° at higher wind speeds due to elastic deflections of the PLA structure. These results, consistent with the prototype scale, validate its usefulness in capturing essential aerodynamic and structural behaviors of the wind turbine. They also demonstrate its relevance as a new tool for experimental studies of wind turbines and open up new research, validation, and control possibilities not considered in previous developments by incorporating blade pitch control. Full article

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

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25 pages, 4694 KB

Open AccessArticle

Research on Fractional-Order Sliding Mode Control of Fractional-Order Permanent Magnet Direct-Drive Wind Power System

by Junhua Xu, Yue Lan, Chunwei Wang, Bin Liu, Yingheng Li and Yongzeng Xie

Machines 2025, 13(10), 928; https://doi.org/10.3390/machines13100928 - 8 Oct 2025

A large number of practical systems show pronounced fractional-order features. In comparison with integer-order calculus, fractional-order calculus has been demonstrated to possess enhanced precision in the description of the dynamic behavior of complex systems. The increase in control accuracy and flexibility results from [...] Read more.

A large number of practical systems show pronounced fractional-order features. In comparison with integer-order calculus, fractional-order calculus has been demonstrated to possess enhanced precision in the description of the dynamic behavior of complex systems. The increase in control accuracy and flexibility results from this improvement. This study explores a direct-drive wind power generation system featuring permanent magnets, which incorporates fractional-order direct current bus (DC-bus) capacitor and fractional-order inductor–capacitor–inductor (FOLCL) grid-connected filter. For the machine-side rectifier, a fractional-order sliding mode (FOSM) speed outer-loop control and a fractional-order proportional–integral (FOPI) current inner-loop control were designed. A voltage outer-loop control using FOSM and a current inner-loop control using FOPI were developed for the grid-side inverter. Through simulation analyses under various wind speeds and grid fault conditions, it is demonstrated that compared to a control strategy using FOPI controllers in both inner and outer loops, the proposed control scheme which employs a FOSM outer-loop and reduces the overshoot of DC-bus voltage and grid-connected current by 21.51% and 32.49%, respectively, under sudden wind speed changes. Furthermore, during grid voltage sag faults, the maximum drop in DC-bus voltage and grid-connected active power are reduced by 65.38% and 33.38%, respectively. These results highlight the proposed method’s superior dynamic and static performance, as well as enhanced resistance to disturbances. Full article

(This article belongs to the Section Electromechanical Energy Conversion Systems)

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3 pages, 158 KB

Open AccessEditorial

Design and Manufacturing: An Industry 4.0 Perspective

by Panagiotis Kyratsis, Angelos P. Markopoulos, Henrique de Amorim Almeida and Tatjana Spahiu

Machines 2025, 13(10), 927; https://doi.org/10.3390/machines13100927 - 8 Oct 2025

Industrial investments include the use of advanced technologies which refine, accelerate, improve quality, and boost stakeholder profitability [...] Full article

15 pages, 6971 KB

Open AccessArticle

Comparative Study of TriVariant and Delta Three-Degree-of-Freedom Parallel Mechanisms for Aerial Manipulation

by Zhujin Jiang, Yihao Lin, Yueyuan Zhang, Mingxiang Ling and Chao Liu

Machines 2025, 13(10), 926; https://doi.org/10.3390/machines13100926 - 7 Oct 2025

The operational performance of robotic arms for multi-rotor flying robots (MFRs) has attracted growing attention in recent years. To explore new possibilities for aerial manipulation, this study investigates a novel parallel mechanism, the TriVariant, comprising one UP limb and two identical UPS limbs [...] Read more.

The operational performance of robotic arms for multi-rotor flying robots (MFRs) has attracted growing attention in recent years. To explore new possibilities for aerial manipulation, this study investigates a novel parallel mechanism, the TriVariant, comprising one UP limb and two identical UPS limbs (2-UPS&UP). To evaluate its potential, we analyze its dimensional and kinematic characteristics and benchmark them against the widely adopted Delta robot, which is commonly integrated with unmanned aerial vehicles (UAVs). A prototype of the TriVariant is fabricated for experimental validation. Both analytical and experimental results reveal that, within a cylindrical task workspace characterized by a large diameter and moderate height, the TriVariant offers a more compact structure than the Delta robot, despite its slightly reduced dexterity. These findings highlight that the TriVariant is especially suitable for aerial manipulation in space-constrained environments where all limbs must be mounted beneath the UAV. Full article

(This article belongs to the Special Issue Smart UAV Machines for Detection and Classification: Advancements and Applications in Renewable Energy Technologies)

19 pages, 9059 KB

Open AccessArticle

Data–Model Integration-Driven Temperature Rise Prediction Method for New Energy Electric Drive Bearings

by Fang Yang, Xi Chen, Zhidan Zhong, Jun Ye and Weiqi Zhang

Machines 2025, 13(10), 925; https://doi.org/10.3390/machines13100925 - 7 Oct 2025

Accurate prediction of bearing temperature rise offers essential support for equipment operation and optimized design. However, traditional methods often lack accuracy under the complex operating conditions of new energy electric drive bearings. To address this, we propose a model–data integration-driven approach for predicting [...] Read more.

Accurate prediction of bearing temperature rise offers essential support for equipment operation and optimized design. However, traditional methods often lack accuracy under the complex operating conditions of new energy electric drive bearings. To address this, we propose a model–data integration-driven approach for predicting the temperature rise in new energy electric drive bearings. First, a data-driven optimization method is employed to integrate mathematical and simulation models, generating highly reliable simulation data. Then, the simulation data and measured data are fused to construct an integrated dataset for bearing temperature rise. Finally, a CNN-LSTM prediction model is established and trained using this dataset. Validation experiments were carried out on the EV6206E-2RZTN/C3 bearing to verify the effectiveness of the proposed method. Results show (1) under constant operating conditions, the MAE during the temperature rise phase is 0.773 °C, and the steady-state phase maximum MAE is 0.686 °C, and (2) under variable operating conditions, the maximum MAE during the temperature rise phase is 0.713 °C, and the steady-state phase maximum MAE is 0.764 °C. The proposed method achieves effective prediction of temperature rise in electric drive bearings and offers a valuable reference for addressing temperature prediction challenges under complex operational conditions. Full article

(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)

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22 pages, 2388 KB

Open AccessArticle

Evaluation of Operational Energy Efficiency for Bridge Cranes Based on an Improved Multi-Strategy Fusion RRT Algorithm

by Quanwei Wang, Xiaoyang Wang, Ziya Ji, Weili Liu, Yingying Fang, Jiayi Hou, Xuying Liu and Hao Wen

Machines 2025, 13(10), 924; https://doi.org/10.3390/machines13100924 - 7 Oct 2025

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, [...] Read more.

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, the improved algorithm introduces heuristic path information to guide the sampling process, enhancing the quality of sampled nodes. By defining a heuristic boundary, the search space is constrained to goal-relevant regions, thereby improving path planning efficiency. Secondly, focused sampling and reconnection strategies are adopted to significantly enhance path quality while ensuring the global convergence of the algorithm. Combined with line segment sampling and probability control strategies, the algorithm balances global exploration and local refinement, further optimizing path selection. Finally, Bezier curves are applied to smooth the generated path, markedly improving path smoothness and feasibility. Comparative experiments conducted on a constructed three-dimensional simulation platform demonstrate that, compared to other algorithms, the proposed algorithm achieves significant optimization in planning time, path cost, number of path nodes, and number of random tree nodes, while generating smoother paths. Notably, under different operational modes, this study provides a quantitative evaluation of operational efficiency and energy consumption based on energy efficiency trade-offs, offering an effective technical solution for the intelligent operation of bridge cranes. Full article

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

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19 pages, 7767 KB

Open AccessArticle

Fabric Flattening with Dual-Arm Manipulator via Hybrid Imitation and Reinforcement Learning

by Youchun Ma, Fuyuki Tokuda, Akira Seino, Akinari Kobayashi, Mitsuhiro Hayashibe and Kazuhiro Kosuge

Machines 2025, 13(10), 923; https://doi.org/10.3390/machines13100923 - 6 Oct 2025

Fabric flattening is a critical pre-processing step for automated garment manufacturing. Most existing approaches employ single-arm robotic systems that act at a single contact point. Due to the nonlinear and deformable dynamics of fabric, such systems often require multiple actions to achieve a [...] Read more.

Fabric flattening is a critical pre-processing step for automated garment manufacturing. Most existing approaches employ single-arm robotic systems that act at a single contact point. Due to the nonlinear and deformable dynamics of fabric, such systems often require multiple actions to achieve a fully flattened state. This study introduces a dual-arm fabric-flattening method based on a cascaded Proposal–Action network with a hybrid training framework. The PA network is first trained through imitation learning from human demonstrations and is subsequently refined through reinforcement learning with real-world flattening feedback. Experimental results demonstrate that the hybrid training framework substantially improves the overall flattening success rate compared with a policy trained only on human demonstrations. The success rate for a single flattening operation increases from 74% to 94%, while the overall success rate improves from 82% to 100% after two rounds of training. Furthermore, the learned policy, trained exclusively on baseline fabric, generalizes effectively to fabrics with varying thicknesses and stiffnesses. The approach reduces the number of required flattening actions while maintaining a high success rate, thereby enhancing both efficiency and practicality in automated garment manufacturing. Full article

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

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18 pages, 4521 KB

Open AccessArticle

Lightweight Design and Research of Electric Towing Winch Based on Kriging-NSGA-III-TOPSIS Multi-Objective Optimization Technology

by Quanliang Liu, Lu Feng, Ya Wang, Ji Lin and Linsen Zhu

Machines 2025, 13(10), 922; https://doi.org/10.3390/machines13100922 - 6 Oct 2025

To address the challenges of weight redundancy, low material utilization, and excessive performance margins in the design of electric cable-hauling machines, this study proposes a novel multi-objective optimization framework. The framework integrates Latin hypercube experimental design, Kriging surrogate modeling, a Non-dominated Sorting Genetic [...] Read more.

To address the challenges of weight redundancy, low material utilization, and excessive performance margins in the design of electric cable-hauling machines, this study proposes a novel multi-objective optimization framework. The framework integrates Latin hypercube experimental design, Kriging surrogate modeling, a Non-dominated Sorting Genetic Algorithm III (NSGA-III), and a coupled TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach. A high-fidelity finite element model based on extreme operating conditions was established to simulate the performance of the electric towing winch. The Kriging model was employed to replace time-consuming finite element calculations, significantly improving computational efficiency. The NSGA-III algorithm was then utilized to search for the Pareto front, identifying a set of optimal solutions that balance multiple design objectives. Finally, the TOPSIS method was applied to select the most preferable solution from the Pareto front. The results demonstrate a 7.32% reduction in the overall mass of the towing winch, a 7.34% increase in the safety factor, and a 4.57% reduction in maximum structural deformation under extreme operating conditions. These findings validate the effectiveness of the proposed Kriging-NSGA-III-TOPSIS strategy for lightweight design of ship deck winch machinery. Full article

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

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23 pages, 1579 KB

Open AccessReview

Multisensor Data Fusion-Driven Digital Twins in Computer Numerical Control Machining: A Review

by Yang Cao

Machines 2025, 13(10), 921; https://doi.org/10.3390/machines13100921 - 6 Oct 2025

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product [...] Read more.

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development. Full article

(This article belongs to the Special Issue Smart Tools in Advanced Machining)

25 pages, 2295 KB

Open AccessArticle

Vehicle Wind Noise Prediction Using Auto-Encoder-Based Point Cloud Compression and GWO-ResNet

by Yan Ma, Jifeng Wang, Zuofeng Pan, Hongwei Yi, Shixu Jia and Haibo Huang

Machines 2025, 13(10), 920; https://doi.org/10.3390/machines13100920 - 5 Oct 2025

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model [...] Read more.

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

(This article belongs to the Section Vehicle Engineering)

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20 pages, 7686 KB

Open AccessArticle

Effect of Cutting Tool Structures on CFRP Interlaminar Drilling

by Peng Yang, Qingqing Li, Shujian Li, Pengnan Li and Tengfei Chang

Machines 2025, 13(10), 919; https://doi.org/10.3390/machines13100919 - 5 Oct 2025

The interlaminar drilling of CFRPs is a new machining method different from traditional drilling, in which the feed direction of the drill bit is parallel to the interlayer interface. To reasonably select tools for CFRP interlaminar drilling, four different types of tool structures, [...] Read more.

The interlaminar drilling of CFRPs is a new machining method different from traditional drilling, in which the feed direction of the drill bit is parallel to the interlayer interface. To reasonably select tools for CFRP interlaminar drilling, four different types of tool structures, including twist drills, dagger drills, candlestick drills, and step drills, are employed to conduct interlaminar drilling. The axial force and the morphologies of material damage are extracted, the comprehensive damage factors are calculated, and the relation among tool structures, machining parameters, and outlet damage is analyzed. Results show that the peak axial force induced by the four types of tool structures reduces sequentially. The dagger drill and the candlestick drill tend to cause burrs and large-area surface tears, respectively, while the twist drill and the step drill will lead to more significant 3D tears. Among the four tools, the average comprehensive damage factor produced by twist drills is the smallest, making it more suitable for CFRP interlaminar drilling. In addition, this study establishes a mathematical prediction model for the peak axial force and the comprehensive damage factor and optimizes the process parameter combination of twist drills, with the spindle speed set to 4732.87 r/min and the feed speed to 0.137 mm/r. Full article

(This article belongs to the Section Advanced Manufacturing)

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25 pages, 12754 KB

Open AccessArticle

Accelerated Life Test and Performance Degradation Test of Harmonic Drive with Failure Analysis

by Xian Zhang, Changming Zhang, Peng Wang, Fan Yang, Chunlei Peng and Xialun Yun

Machines 2025, 13(10), 918; https://doi.org/10.3390/machines13100918 - 5 Oct 2025

Harmonic drive is susceptible to strength failure and performance degradation failure during operation. Given the long life test cycles, limited sample size, and incomplete understanding of degradation laws, this study conducted comprehensive life test and performance degradation test research to enable future failure [...] Read more.

Harmonic drive is susceptible to strength failure and performance degradation failure during operation. Given the long life test cycles, limited sample size, and incomplete understanding of degradation laws, this study conducted comprehensive life test and performance degradation test research to enable future failure prediction and reliability assessment for harmonic drive. Building upon established test rigs for a HD life test and performance degradation test, a step-down accelerated life test methodology was developed. Life tests under step-down accelerated conditions were executed, with a concurrent performance degradation test throughout the life test. Key datasets acquired include vibration signal histories, degradation data for critical performance indicators such as stiffness, precision, transmission efficiency, and backlash. Test results show that the predominant strength failure in the atmospheric environment is flexspline fatigue fracture, while significant degradation occurred in stiffness, precision, and backlash across all test conditions; transmission efficiency showed gradual degradation before strength failure followed by a marked decline post-failure. Finally, the failure mechanism of strength and performance degradation is analyzed, and the mechanism consistency of the two failures is verified by Hilbert envelope spectrum analysis and degradation trajectory shape consistency, respectively. The results from this paper provide critical data support and a reference foundation for the proactive maintenance of the harmonic drive. Full article

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

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19 pages, 1868 KB

Open AccessArticle

Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification

by Yun Zhang, Mingchen Luan, Zhenyu Tang, Haitao Yan and Long Wang

Machines 2025, 13(10), 917; https://doi.org/10.3390/machines13100917 - 5 Oct 2025

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model [...] Read more.

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

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

26 pages, 7856 KB

Open AccessArticle

Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots

by Weihua Chen, Yehao Feng, Tie Zhang and Canlin Peng

Machines 2025, 13(10), 916; https://doi.org/10.3390/machines13100916 - 4 Oct 2025

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters [...] Read more.

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC’s prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. Full article

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

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20 pages, 2696 KB

Open AccessArticle

Adaptive Backstepping Control of an Unmanned Aerial Manipulator

by Muhammad Awais Rafique, Mohssen E. Elshaar and Alan F. Lynch

Machines 2025, 13(10), 915; https://doi.org/10.3390/machines13100915 - 4 Oct 2025

This paper presents an adaptive backstepping feedback control design for an unmanned aerial manipulator (UAM) that consists of an unmanned aerial vehicle (UAV) with an attached robotic arm. The effect of the arm is treated as a disturbance force and torque, as well [...] Read more.

This paper presents an adaptive backstepping feedback control design for an unmanned aerial manipulator (UAM) that consists of an unmanned aerial vehicle (UAV) with an attached robotic arm. The effect of the arm is treated as a disturbance force and torque, as well as a parametric uncertainty in inertial parameters. The proposed adaptive law guarantees disturbance rejection assuming constant parameters and disturbances. In practice, this assumption includes the case of fixed-arm configurations. To validate the control design, numerical simulations are performed, including a realistic pick-and-place scenario. Full article

(This article belongs to the Special Issue Nonlinear Dynamics in Mechanical Systems, Automation and Control)

26 pages, 2546 KB

Open AccessArticle

Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion

by Fang Yang, En Dong, Zhidan Zhong, Weiqi Zhang, Yunhao Cui and Jun Ye

Machines 2025, 13(10), 914; https://doi.org/10.3390/machines13100914 - 3 Oct 2025

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, [...] Read more.

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article

(This article belongs to the Special Issue Data-Driven RUL Prediction: Innovations in Generalization, Uncertainty, and Efficiency for Industrial PHM)

21 pages, 1662 KB

Open AccessArticle

Influence of Vibration on Servo Valve Performance and Vibration Suppression in Electro-Hydraulic Shaking Table

by Tao Wang, Sizhuo Liu, Zhenyu Guo and Yuelei Lu

Machines 2025, 13(10), 913; https://doi.org/10.3390/machines13100913 - 3 Oct 2025

With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the [...] Read more.

With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the entire hydraulic system, the quality of its own characteristics has a significant impact on the normal operation and safety of the mechanical equipment. Therefore, the working stability of the servo valve in actual operation is of great importance to its body and the overall servo system. Similarly, during the vibration test of the electro-hydraulic servo shaking table, servo valve inevitably experiences various vibrations and shocks, which requires the servo system to be able to withstand the test and assessment under the extreme conditions in actual operation to ensure the smooth operation. This paper takes function of the shaker as the research target and studies the servo valve under various vibration conditions by constructing a digital modeling system. On this basis, an adaptive format filter is established, and corresponding vibration suppression methods are adopted for the vibration conditions inside the system. Finally, simulation examples are used to prove that this method can more effectively control the vibration in the servo valve and suppress the interference with shaking table function. Full article

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

20 pages, 5885 KB

Open AccessArticle

Geometric Design and Basic Feature Analysis of Double Helical Face Gears

by Xiaomeng Chu and Faqiang Chen

Machines 2025, 13(10), 912; https://doi.org/10.3390/machines13100912 - 3 Oct 2025

This study aims to address the problem that traditional helical gears generate significant axial forces during transmission and innovatively proposes a design scheme of double helical face gears (DHFG). An accurate mathematical model of the tooth surface is established using spatial meshing theory [...] Read more.

This study aims to address the problem that traditional helical gears generate significant axial forces during transmission and innovatively proposes a design scheme of double helical face gears (DHFG). An accurate mathematical model of the tooth surface is established using spatial meshing theory and coordinate transformation. A systematic investigation using the orthogonal test method is then conducted to analyze the influence of key parameters, such as the pinion tooth number, transmission ratio, and helix angle, on gear performance. The finite element analysis results show that the overlap degree of this double helical tooth surface gear pair in actual transmission can reach 2–3, demonstrating excellent transmission smoothness. More importantly, its unique symmetrical tooth surface structure successfully achieves the self-balancing effect of axial force. Simulation verification shows that the axial force is reduced by approximately 70% compared to traditional helical tooth surface gears, significantly reducing the load on the bearing. Finally, the prototype gear is successfully trial-produced through a five-axis machining center. Experimental tests confirmed that the contact impressions are highly consistent with the simulation results, verifying the feasibility of the design theory and manufacturing process. Full article

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

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18 pages, 3114 KB

Open AccessArticle

A Novel Empirical-Informed Neural Network Method for Vehicle Tire Noise Prediction

by Peisong Dai, Ruxue Dai, Yingqi Yin, Jingjing Wang, Haibo Huang and Weiping Ding

Machines 2025, 13(10), 911; https://doi.org/10.3390/machines13100911 - 2 Oct 2025

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance [...] Read more.

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance and shortening the development cycle. Although the data-driven machine learning method has been widely used in automobile noise research due to its advantages of no need for accurate physical modeling, data learning and generalization ability, it still faces the challenge of insufficient accuracy in capturing key local features, such as peaks, in practical NVH engineering. Aiming at this challenge, this paper introduces a forecast approach that utilizes an empirical-informed neural network, which aims to integrate a physical mechanism and a data-driven method. By deeply analyzing the transmission path of interior noise, this method embeds the acoustic mechanism features such as local peak and noise correlation into the deep neural network as physical constraints; therefore, this approach significantly enhances the model’s predictive performance. Experimental findings indicate that, in contrast to conventional deep learning techniques, this method is able to develop better generalization capabilities with limited samples, while still maintaining prediction accuracy. In the verification of specific models, this method shows obvious advantages in prediction accuracy and computational efficiency, which verifies its application value in practical engineering. The main contributions of this study are the proposal of an empirical-informed neural network that embeds vibro-acoustic mechanisms into the loss function and the introduction of an adaptive weight strategy to enhance model robustness. Full article

(This article belongs to the Section Vehicle Engineering)

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Open AccessArticle

Effects of Roller End/Rib Curvature Ratio on Friction and Accuracy in Tapered Roller Bearings

by Wenhu Zhang and Gang Li

Machines 2025, 13(10), 910; https://doi.org/10.3390/machines13100910 - 2 Oct 2025

To address the uncertainty in selecting the optimal spherical base curvature radius (SR) for tapered roller bearings, this study develops dynamic and friction torque models under combined loading conditions to evaluate three SR configurations—0.85ρ_p, 0.90ρ_p, [...] Read more.

To address the uncertainty in selecting the optimal spherical base curvature radius (SR) for tapered roller bearings, this study develops dynamic and friction torque models under combined loading conditions to evaluate three SR configurations—0.85ρ_p, 0.90ρ_p, and 0.95ρ_p, where ρ_p represents the curvature radius of the inner rib—in terms of load capacity, friction losses, and operational precision. The results indicate that (1) the 0.85ρ_p configuration minimizes friction by optimizing the contact zone’s f_V parameter under combined loads, making it ideal for low-friction applications; (2) the 0.95ρ_p design achieves superior operational accuracy; (3) the intermediate value of 0.90ρ_p offers an optimal compromise, balancing friction torque reduction with operational precision. These findings establish quantitative guidelines for SR selection based on specific bearing performance requirements. Full article

(This article belongs to the Section Friction and Tribology)

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