Machines

25 pages, 6554 KB

Open AccessArticle

Comparative Thermal Performance Evaluation of Compact Magnetic Gears with High-Saturation Magnetic Alloys for High-Speed Applications

by Kadir Yılmaz, Taner Dindar, Ufuk Ayhan, Murat Ayaz, Serkan Aktaş and Serkan Sezen

Machines 2026, 14(7), 760; https://doi.org/10.3390/machines14070760 - 6 Jul 2026

Abstract

Coaxial magnetic gears (CMGs) have emerged as a promising alternative to conventional mechanical gear systems due to their contactless torque transmission, low maintenance requirements, and high reliability. However, under high-speed operation, conductivity-induced eddy current losses become dominant and significantly limit thermal performance. This [...] Read more.

Coaxial magnetic gears (CMGs) have emerged as a promising alternative to conventional mechanical gear systems due to their contactless torque transmission, low maintenance requirements, and high reliability. However, under high-speed operation, conductivity-induced eddy current losses become dominant and significantly limit thermal performance. This study comparatively investigates the coupled electromagnetic and thermal behavior of two compact CMGs with identical torque capacity using M400-50A electrical steel and cobalt-based Hiperco 50A. Coupled electromagnetic–thermal finite element analyses are performed from 1000 to 12,000 rpm under worst-case natural convection conditions. The results demonstrate that the use of Hiperco 50A reduces core losses by up to 71% at high speeds and enables approximately 7.3% greater volumetric compactness owing to its higher saturation capability. Eddy-current-related losses remain the dominant loss mechanism at elevated speeds, causing inner-rotor permanent-magnet temperatures to exceed the allowable limits of NdFeB materials. Under natural convection, the outer permanent-magnet temperature remains below the critical threshold of 200 °C up to approximately 5800 rpm for the M400-50A design. With Hiperco 50A, this limit increases to approximately 6000 rpm under identical operating conditions, corresponding to an improvement of about 3%. These findings demonstrate the thermal benefits of high-saturation magnetic alloys; however, additional cooling strategies are required for operation at higher speeds. Full article

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

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23 pages, 16197 KB

Open AccessArticle

An Improved Mesh Stiffness Model for Cracked Spur Gears Considering Tooth Surface Contact Characteristics

by Shihua Zhou, Xuan Li, Chenhui Zhou, Tengyuan Xu, Ye Zhang and Zhaohui Ren

Machines 2026, 14(7), 759; https://doi.org/10.3390/machines14070759 - 6 Jul 2026

Abstract

Tooth crack, as a typical fault, directly affects the meshing characteristics of gears, which causes abnormal vibration and noise during the gear meshing process, with some even threatening the operational safety of the mechanical device. Meanwhile, the mapping relation between the tooth crack [...] Read more.

Tooth crack, as a typical fault, directly affects the meshing characteristics of gears, which causes abnormal vibration and noise during the gear meshing process, with some even threatening the operational safety of the mechanical device. Meanwhile, the mapping relation between the tooth crack and the actual meshing characteristics is still unclear under the tooth surface morphology and lubrication properties. Aiming at this issue, an integrated time-varying meshing stiffness (I-TVMS) model with cracks is proposed under the complex and variable working conditions. Based on the potential energy method, the analytical expressions with cracks are derived and calculated, and, then, the variation laws of I-TVMS under different crack parameters, tooth surface morphology, and structural and excitation parameters are investigated. Combined with the healthy tooth, the crack increases the contact load on the tooth surface, and reduces the oil film thickness, which decreases the I-TVMS of the cracked tooth. The greater the crack depth and torque is, the smaller the oil film thickness, and the weaker the I-TVMS fluctuation will be. The influence of the crack angle depends on the crack type and meshing region. The tooth-root crack is more sensitive in the single-tooth region, whereas the tooth surface crack shows a larger change only in the double-tooth mean value. When the crack location transitions from the tooth root to the tooth top, the stiffness attenuation gradually weakens. Full article

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

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27 pages, 19736 KB

Open AccessArticle

SEDR-Net: A YOLOv11-Based Network for Conveyor Belt Surface Defect Detection in Complex Industrial Scenes

by Fei Cong, Yiping Yuan, Lu Xiao, Tingting Wang and Weiwei Han

Machines 2026, 14(7), 758; https://doi.org/10.3390/machines14070758 - 6 Jul 2026

Abstract

Belt conveyors are essential to continuous material transport systems, and reliable surface defect detection is therefore critical for safe and stable operation. In real industrial environments, defects such as tears, punctures, and localized damage are often small, elongated, and characterized by weak boundary [...] Read more.

Belt conveyors are essential to continuous material transport systems, and reliable surface defect detection is therefore critical for safe and stable operation. In real industrial environments, defects such as tears, punctures, and localized damage are often small, elongated, and characterized by weak boundary contrast. Complex background interference further increases the difficulty of accurate and reliable detection for real-time defect detectors. To address these challenges, this paper proposes SEDR-Net (Structure-Edge and Detail Reconstruction Network), a YOLOv11n-based network, for this task. The Structural-Edge Fusion Block (SEFBlock), Channel-Spatial Collaborative Attention (CSCA), and Efficient Up-Convolution Block (EUCB) respectively enhance structural-edge representation, suppress redundant background responses, and recover local structures and boundary details. On a public conveyor-belt defect dataset, SEDR-Net achieves 90.8% Recall, with an mAP@0.5 of 92.4% and an mAP@0.5:0.95 of 58.4%, yielding improvements of 4.7, 3.8, and 7.1 percentage points over YOLOv11n, respectively. Meanwhile, SEDR-Net uses 2.42 M trainable parameters and maintains an inference speed of 134.2 FPS, indicating a favorable accuracy–complexity trade-off for real-time inspection. An independent external industrial test set further verifies the cross-scenario robustness and practical applicability of the proposed method under real mining conveyor-belt conditions. Full article

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

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20 pages, 5213 KB

Open AccessArticle

Modeling and Selection of Rational Parameters for Sensors Installation Assemblies on Coal Charging Car Hoppers

by Volodymyr Lipovskyi, Kostiantyn Baiul, Pavlo Krot, Serhii Vashchenko, Olexander Khudyakov and Yurii Semenov

Machines 2026, 14(7), 757; https://doi.org/10.3390/machines14070757 - 6 Jul 2026

Abstract

This study presents a comprehensive analysis of the modeling and optimization of sensor installation nodes for weight measurement in the hoppers of a charging car utilized in coke production. The research highlights the critical role of precise load monitoring in preventing technological disruptions, [...] Read more.

This study presents a comprehensive analysis of the modeling and optimization of sensor installation nodes for weight measurement in the hoppers of a charging car utilized in coke production. The research highlights the critical role of precise load monitoring in preventing technological disruptions, minimizing equipment degradation, and optimizing energy consumption. Conventional sensor technologies, including capacitive, ultrasonic, and laser-based systems, are evaluated, with weight sensors mounted on hopper supports identified as the most robust solution for real-time mass determination under industrial conditions characterized by high dust levels, temperature fluctuations, and mechanical vibrations. A finite element analysis (FEA) was conducted to assess the structural behavior of sensor installation nodes under three distinct loading scenarios, corresponding to different operational conditions of the charging car. The four-point support structure of the hopper experienced the highest loads and non-uniformities. A stress–strain analysis of the sensor mounting assembly, performed using the Ansys software package, confirmed that both the sensor and its support structure maintain a sufficient safety margin (version 2024 R1, Ansys Inc., Canonsburg, PA, USA, the academic license provided to Wrocław University of Science and Technology). The findings validate the structural integrity and operational reliability of the proposed sensor configuration, contributing to the advancement of automated monitoring and control systems in coke production. Full article

(This article belongs to the Special Issue Trends and Challenges in the Development and Operation of Modern Vehicles)

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18 pages, 2971 KB

Open AccessArticle

AI-Driven Prediction of Surface Roughness and Cutting Force in Milling Aluminum Alloy Under Data-Scarce Conditions

by Mohammad Hossein Ebrahimi and Seyed Ali Niknam

Machines 2026, 14(7), 756; https://doi.org/10.3390/machines14070756 - 5 Jul 2026

Abstract

Accurate prediction of surface roughness and cutting forces in milling aluminum alloys remains challenging under data-scarce conditions, where limited experimental data restricts the application of conventional machine learning models. This study addresses this gap by developing a systematic machine learning framework using 108 [...] Read more.

Accurate prediction of surface roughness and cutting forces in milling aluminum alloys remains challenging under data-scarce conditions, where limited experimental data restricts the application of conventional machine learning models. This study addresses this gap by developing a systematic machine learning framework using 108 milling experiments (repeated to 216 tests) on aluminum alloys AA2024-T351 and AA6061-T6. Five primary machining inputs—material type, spindle speed, feed rate, depth of cut, and tool coating—were used. Through feature engineering, 35 interaction features were generated to capture non-linear relationships. A two-step preprocessing strategy was applied: Winsorization at the 5th and 95th percentiles to handle outliers, followed by hybrid scaling combining RobustScaler and MinMaxScaler. Eight machine learning algorithms, including XGBoost, NGBoost, LightGBM, CatBoost, Random Forest, MLP, SVR, and Least Squares Boosting, were developed and hyperparameter-optimized using the Optuna framework with Tree-structured Parzen Estimator. Models were evaluated using R², MAE, and RMSE on a 70/15/15 train–validation–test split. Results demonstrate that XGBoost achieved the highest predictive accuracy for surface roughness (Ra) (R² = 0.99829) and for resultant cutting force (FN) (R² = 0.997). Feed rate was identified as the dominant machining parameter, accounting for 87.7% of the total importance in predicting surface roughness. SHAP analysis confirmed that engineered interaction features—particularly Feed_Coating and Material_Feed—carry strong physical relevance. Additionally, NGBoost enabled probabilistic regression, providing uncertainty estimates. The proposed framework proves highly effective for multi-output prediction in machining under limited data, offering a robust, interpretable, and industry-ready solution for quality control in aluminum alloy milling operations. Full article

(This article belongs to the Special Issue Tool Wear Condition Monitoring in Smart Manufacturing: Sensors, Analytics, and Decision-Making)

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38 pages, 17459 KB

Open AccessArticle

Padé Approximant Neural Networks as Feature Extractors for Unsupervised Domain Adaptation in Bearing Fault Diagnosis

by Sertac Kilickaya, Cansu Celebioglu, Murat Askar, Turker Ince and Levent Eren

Machines 2026, 14(7), 755; https://doi.org/10.3390/machines14070755 - 5 Jul 2026

Abstract

Variations in mechanical load constitute a dominant source of domain shift in data-driven fault diagnosis of rotating machinery, causing models trained under one operating condition to degrade sharply when deployed under another. This work addresses the problem through unsupervised domain adaptation (UDA)—which transfers [...] Read more.

Variations in mechanical load constitute a dominant source of domain shift in data-driven fault diagnosis of rotating machinery, causing models trained under one operating condition to degrade sharply when deployed under another. This work addresses the problem through unsupervised domain adaptation (UDA)—which transfers diagnostic knowledge from a labeled source condition to an unlabeled target condition by aligning their feature distributions—and introduces Padé Approximant Neural Networks (PadéNets) as compact yet highly expressive feature extractors. One-dimensional PadéNet encoders are embedded into three established adaptation frameworks—Deep CORAL, Domain-Adversarial Neural Networks (DANNs), and Conditional Domain-Adversarial Networks (CDANs)—to learn load-invariant representations without any labeled target data. On the Case Western Reserve University benchmark, where the models operate directly on raw time-domain vibration signals, replacing conventional convolutional encoders with PadéNets consistently improves cross-load diagnostic accuracy, reaching up to 99.28% average target-domain accuracy at a low parameter count. To assess generalization to a more demanding setting, the CDAN–PadéNet configuration is further evaluated on frequency-domain representations of the Paderborn University dataset, where domain shift arises from simultaneous variation of load torque and radial force on bearings with real accelerated-lifetime damage, attaining 99.84% average accuracy across six cross-condition transfer tasks while requiring fewer parameters than competing methods. These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms. Full article

(This article belongs to the Special Issue Diagnostics and Fault Detection in Induction Motors: Trends and Applications)

24 pages, 3406 KB

Open AccessArticle

Design and Motion Control Strategies for an Omniwheel System

by Jiaqi Duan, Zelin Yang, Jiankang Zhi, Jian Zhao, Shize Qin, Yanbo Wang and Baosen Du

Machines 2026, 14(7), 754; https://doi.org/10.3390/machines14070754 - 5 Jul 2026

Abstract

Space debris mitigation is a pivotal endeavor essential for sustaining human space exploration. To address the challenges posed by irregularly shaped, variably sized, and dynamically unpredictable debris in orbit, this paper proposes a mechanical design and motion control strategy for an omniwheel-based driving [...] Read more.

Space debris mitigation is a pivotal endeavor essential for sustaining human space exploration. To address the challenges posed by irregularly shaped, variably sized, and dynamically unpredictable debris in orbit, this paper proposes a mechanical design and motion control strategy for an omniwheel-based driving system. The mechanical architecture and kinematic principles of the system are elaborated in detail, complemented by the formulation of tailored motion control algorithms. First, the fundamental architecture of the driving subsystem is introduced, and the linear mapping between the uniformly distributed triad of omniwheels and the spherical drive is derived. Building upon this foundation, the kinematic transmission from the three evenly spaced driving subsystems to the contact sphere is established. This leads to the derivation of the overall linear mapping relationship between the nine uniformly distributed omniwheels and the contact sphere’s motion, thereby enabling precise trajectory tracking of the contact sphere via omniwheel actuation. Finally, comprehensive experimental validation was conducted in two phases. The first phase evaluated the fidelity and stability of the driving subsystem’s simulation model, as well as the accuracy of the kinematic mapping. Results demonstrate that the simulation model is highly stable and reliable. Under identical desired trajectories, the Root Mean Square Error (RMSE) between theoretical calculations and simulations was 4.082 × 10⁻⁴, while the RMSE between theory and physical prototypes was 0.0032. These results confirm that the motion errors remain within acceptable tolerances and the kinematic mapping is accurate. For the spherical end-effector, under the same trajectory conditions, the RMSE values among theoretical calculations, simulations, and physical prototypes were 0.0929 and 1.62, respectively. These findings validate the derived linear kinematic mapping, demonstrating its efficacy in precise motion control, which lays the foundation for future on-orbit detumbling tasks. Full article

(This article belongs to the Special Issue Smart Structures and Applications in Aerospace Engineering)

31 pages, 7578 KB

Open AccessArticle

Research on the Influence of Piston Pair Wear on Pump Output Characteristics of Axial Piston Pump Under Multiple Working Conditions

by Sibo Liu, Hongwang Zhao, Dandan Wu, Jiabao Li, Hao Li and Zhong Liu

Machines 2026, 14(7), 753; https://doi.org/10.3390/machines14070753 - 4 Jul 2026

Abstract

To clarify the nonlinear degradation of volumetric performance caused by piston–cylinder wear in axial piston pumps under multiple operating conditions, this study develops an integrated framework linking local wear-induced leakage to whole-pump output characteristics. A mathematical model incorporating piston kinematics, eccentric-clearance leakage, chamber-pressure [...] Read more.

To clarify the nonlinear degradation of volumetric performance caused by piston–cylinder wear in axial piston pumps under multiple operating conditions, this study develops an integrated framework linking local wear-induced leakage to whole-pump output characteristics. A mathematical model incorporating piston kinematics, eccentric-clearance leakage, chamber-pressure dynamics, and whole-pump flow was established and implemented in Amesim. A four-factor mixed-level orthogonal design and analysis of variance were then employed to quantify the effects of wear clearance, eccentricity, load pressure, and shaft rotational speed. The results show that their contributions to volumetric efficiency follow the order: motor rotational speed > load pressure > wear clearance > eccentricity, whereas load pressure is the dominant factor affecting pressure ripple. A wear clearance of approximately 0.1 mm marks the onset of pronounced leakage-induced performance degradation. At this clearance and a load pressure of 20 MPa, increasing the rotational speed from 500 to 3000 r/min improves the volumetric efficiency from 67.48% to 94.44%, with an average increase of 5.39 percentage points per 500 r/min. Comparative experiments on normal and artificially worn pumps were conducted to validate the model. The measured motor-speed slip under high-load conditions was incorporated to correct the theoretical displacement and distinguish speed-induced flow reduction from internal leakage. After correction, the maximum relative error between the simulated and experimental results was below 3.7%. The proposed framework integrates mechanism-based leakage modeling, multi-factor contribution analysis, and speed-corrected experimental validation, providing a theoretical basis for the wear assessment, piston–cylinder clearance design, and flow compensation of axial piston pumps under variable operating conditions. Full article

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

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22 pages, 1644 KB

Open AccessArticle

Vibration Signal-Based Fault Detection and Classification in Friction Stir Welding Process Using Statistical Features and Lazy Learning Classifiers

by Jegadeeshwaran Rakkiyannan, Balachandar Krishnamurthy, Lakshmi Pathi Jakkamputi, Sakthivel Gnanasekaran and Mohanraj Thangamuthu

Machines 2026, 14(7), 752; https://doi.org/10.3390/machines14070752 - 3 Jul 2026

Abstract

This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The [...] Read more.

This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures. Full article

(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)

35 pages, 12347 KB

Open AccessReview

A Review of Electric Machine Stator Winding Insulation Diagnostic Signal Processing Methods and Metrics

by Daniel Addae and Emmanuel Agamloh

Machines 2026, 14(7), 751; https://doi.org/10.3390/machines14070751 - 3 Jul 2026

Abstract

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while [...] Read more.

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while the machine is in operation. These diagnostic methods depend on different signal processing techniques that are used to extract insulation-sensitive information from measured signals. This paper presents a review of the diagnostic signal processing techniques that have been applied to stator winding insulation condition monitoring, spanning time-domain, frequency-domain, time–frequency-domain and data-driven approaches. Where appropriate, the underlying mathematical formulation of the reviewed technique is presented, the physical basis for its sensitivity to insulation condition monitoring is discussed, and the key strengths and limitations are identified. A comparative analysis with summary tables is provided to highlight the trade-offs between detection sensitivity, computational cost, hardware requirements and practical deployment considerations. The review shows that time- and frequency-domain methods are simple to implement, while time–frequency and data-driven methods generally offer higher performance, but require greater computation and validation. Also, the comparison shows that turn-to-turn and groundwall insulation monitoring have received more research attention, while phase-to-phase remains less developed. This review concludes by identifying the challenges and future research directions needed to advance this field from laboratory demonstrations toward industrial adoption. Full article

(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives, 2nd Edition)

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25 pages, 9749 KB

Open AccessArticle

An Integrated Path Planning Algorithm Based on Vector Jump Point Search and Ant Colony Optimization for Quay Crane Inspection

by Haoye Zhang, Zihan Lei, Mingxiao Wang, Zhipeng Hou, Hongren Zhao, Christophe Claramunt, Gang Tang and Weidong Zhu

Machines 2026, 14(7), 750; https://doi.org/10.3390/machines14070750 - 3 Jul 2026

Abstract

Unmanned aerial vehicles have become a promising platform for inspecting large port machinery; however, quay cranes contain sparse but complex steel-frame structures, multiple inspection points, and narrow collision-constrained spaces, which make efficient inspection path planning difficult. Existing approaches often focus either on global [...] Read more.

Unmanned aerial vehicles have become a promising platform for inspecting large port machinery; however, quay cranes contain sparse but complex steel-frame structures, multiple inspection points, and narrow collision-constrained spaces, which make efficient inspection path planning difficult. Existing approaches often focus either on global point sequencing or local collision-free search, and conventional global optimizers usually use straight-line distances that do not reflect obstacle-constrained flight costs. This paper proposes an integrated path planning method for quay crane inspection based on vector jump point search and ant colony optimization. In the local path-searching stage, vector-guided preprocessing and path simplification are used to calculate collision-free paths between mission points and construct a path cost matrix. In the global optimization stage, ant colony optimization determines the inspection sequence using the collision-free cost matrix rather than Euclidean distances. Simulation experiments were conducted on a simplified quay crane model of 132 m × 22 m × 70 m with 25 mission points. The results show that the proposed method reduced the average local path-searching time from 5.3392 s to 4.2907 s, corresponding to a 19.6% improvement over jump point search, while reducing the average local path length by 5.1%. The final global inspection path obtained in the experimental case was 516.1 m, which was shorter than those obtained by simulated annealing, genetic algorithm, particle swarm optimization, and the previous method. These results indicate that the proposed method can improve local planning efficiency and provide an effective inspection route for unmanned aerial vehicle-based quay crane inspection. Full article

(This article belongs to the Special Issue Towards Embodied Intelligence: Novel Kinematic Structures and AI-Guided Mechanism Design)

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58 pages, 2345 KB

Open AccessReview

Overview of Thermal Management System for Hydrogen-Fueled Aero-Engines Driven by Energy Conservation and Digital Intelligence

by Yiqiao Li, Jing Huang, Yang Xiao, Shanlin Liu, Yifei Chen, Luyuan Gong, Yali Guo and Shengqiang Shen

Machines 2026, 14(7), 749; https://doi.org/10.3390/machines14070749 - 2 Jul 2026

Abstract

Under the background of the green transformation and energy conservation in the aviation field, hydrogen-fueled aero-engines are the primary direction for achieving sustainable aviation power development. However, the unique thermophysical properties of hydrogen fuel induce extreme thermal load challenges to engine thermal management. [...] Read more.

Under the background of the green transformation and energy conservation in the aviation field, hydrogen-fueled aero-engines are the primary direction for achieving sustainable aviation power development. However, the unique thermophysical properties of hydrogen fuel induce extreme thermal load challenges to engine thermal management. Based on the requirements of energy conservation and digital-intelligent technologies, this paper reviewed the recent research progress, important challenges, and future development directions in the thermal management field for hydrogen-fueled aero-engines, and filled the gaps in existing related reviews. (1) As for the liquid hydrogen thermal properties and thermal management requirements, the unique thermal physical properties of liquid hydrogen can easily cause fluctuations in heat load, large temperature differences, and material compatibility issues such as hydrogen embrittlement during storage, transportation, and combustion. The application of thermal barrier coatings, the design of targeted cooling structures, and the regulation of heat loss in the pipeline of the hydrogen supply system require particular attention. (2) As for the technical architecture and optimization of thermal management, the optimization of the high-pressure side manifolds in the cooled cooling air heat exchanger increases the flow uniformity by 18.8% and reduces the weight by 22.5%. The intercooled recuperated engine with the optimum area ratio reduces specific fuel consumption by 5.3% compared to the baseline engine in cruise. However, the system-level optimization research of the above widely recognized solutions is relatively limited in terms of coordinating the energy flow of engines. The baseline engine employed the method of system integration optimization to achieve a 2.99% increase in thrust and a 6.78% reduction in fuel consumption. (3) As for the thermal management modeling and simulation, the intelligent optimization method based on computational fluid dynamics reduces the pressure loss coefficient of the vane-integrated heat exchanger by 36%. Nevertheless, the multiphysics coupling model confronts a contradiction between computational cost and accuracy. (4) As for the comprehensive evaluation method, the advanced configuration of the hydrogen-fueled aero-engine can approximately reduce specific fuel consumption by 68.5% and NOx emission by 12.7% under the same maximum thrust condition. The hydrogen consumption of the proton exchange membrane fuel cells system model compared with the baseline system, optimized by the multi-objective optimization algorithm, has decreased by 15%, while the thermal uniformity has improved by 20–30%. However, the current evaluation system mostly focuses on a single dimension, lacking the analysis of nonlinear coupling among multiple factors and a closed-loop mechanism for evaluation, optimization, and verification. Future research should focus on the matching model of liquid hydrogen’s thermophysical properties and full flight conditions, global multi-energy flows optimization methods, multidimensional collaborative numerical simulation, multiphysics coupling models, and multidimensional comprehensive evaluation systems, to provide closed-loop theoretical support for the efficient, intelligent, and reliable thermal management system for hydrogen-fueled aero-engines. Full article

(This article belongs to the Special Issue Machine Tools for Precision Machining: Design, Control and Prospects)

32 pages, 3135 KB

Open AccessArticle

Higher-Order Kinematic Analysis of a Six-Bar Mechanism with a Prismatic Joint: Centrodes and Bresse Circles

by Eddie Gazo-Hanna, Ahmed Saber and Semaan Amine

Machines 2026, 14(7), 748; https://doi.org/10.3390/machines14070748 - 2 Jul 2026

Abstract

Planar linkage mechanisms remain a cornerstone of motion generation and trajectory control, yet the geometric tools that desRcribe their instantaneous behavior, namely centrodes and Bresse’s circles, have been developed almost exclusively for mechanisms with entirely revolute joints, where a sliding pair fundamentally alters [...] Read more.

Planar linkage mechanisms remain a cornerstone of motion generation and trajectory control, yet the geometric tools that desRcribe their instantaneous behavior, namely centrodes and Bresse’s circles, have been developed almost exclusively for mechanisms with entirely revolute joints, where a sliding pair fundamentally alters the velocity and acceleration fields and disrupts the symmetries on which classical curvature theory relies. This paper presents a comprehensive higher-order kinematic analysis of a planar six-link, single-degree-of-freedom mechanism in which a slider-crank stage and a rocker stage are coupled through a shared prismatic joint that acts simultaneously as output and input. Using vector algebra and a matrix-based loop-closure formulation, the position, velocity, and acceleration analyses are derived in closed form, yielding angular velocity ratios, the instantaneous centers of rotation and acceleration of both coupler links, and their inflection and stationarity circles. The analysis reveals a distinctive geometric constraint on the slider-side coupler’s instantaneous center, a decoupling of the curvature loci of the two couplers, and degenerate configurations, linked to coupler instantaneous-stop and rocker dead-point conditions, that arise at joint-invariant crank angles. Implemented as a computational algorithm and demonstrated on a carton flap-closing mechanism and cross-validated against independent multibody simulation, the framework confirms favorable transmission and dead-point clearance behavior, extending curvature-theory tools to linkages containing sliding pairs. Full article

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

35 pages, 1976 KB

Open AccessReview

Soft Robotics: Enabling Technologies, Applications, and Future Perspectives

by Yibo Wang, Mengwei Wu, Bintao Zou, Yimeng Du, Hengxu Du and Pengfei Chen

Machines 2026, 14(7), 747; https://doi.org/10.3390/machines14070747 - 2 Jul 2026

Abstract

Soft robots built from compliant materials and deformable structures are increasingly used in medical intervention, wearable assistance, delicate manipulation, and environmental exploration, where conventional rigid robots are limited by high mechanical impedance and poor morphological adaptability. However, their transition from laboratory prototypes to [...] Read more.

Soft robots built from compliant materials and deformable structures are increasingly used in medical intervention, wearable assistance, delicate manipulation, and environmental exploration, where conventional rigid robots are limited by high mechanical impedance and poor morphological adaptability. However, their transition from laboratory prototypes to deployable systems remains constrained by coupled bottlenecks in materials, actuation, sensing, modeling, control, energy supply, and manufacturing. This review summarizes recent advances in soft robotics through an evaluative framework covering actuation and materials, modeling and simulation, control strategies, multimodal sensing, and representative applications. Instead of treating these topics as independent descriptions, we compare the underlying mechanisms, measurable performance indicators, strengths, limitations, and application boundaries. Three conclusions emerge. First, no single actuation strategy can simultaneously maximize output force, response speed, energy efficiency, durability, miniaturization, and untethered operation. Second, high-fidelity continuum models improve physical accuracy but remain difficult to use for real-time control, whereas reduced-order and data-driven models improve efficiency at the cost of generalization, interpretability, or contact fidelity. Third, practical soft robots will depend on system-level integration of embedded sensing, physics-informed learning, robust control, reliable materials, and scalable fabrication. Future progress should therefore prioritize standardized benchmarks, lifecycle reliability, energy-autonomous operation, and task-specific comparisons with rigid robotic systems. Full article

(This article belongs to the Special Issue Innovations in Soft Robotics: Enhancing Safety, Performance, and Dexterity)

21 pages, 1791 KB

Open AccessArticle

Parametric Study of an H-Shaped-Core Magnetic Field Energy Harvester for Railway Traction-Returning Magnetic Fields

by Tingliang Zhao, Chengcheng Zuo, Zheng Jun Chew and Yang Kuang

Machines 2026, 14(7), 746; https://doi.org/10.3390/machines14070746 - 2 Jul 2026

Abstract

During train operation, railway traction-returning current generates a power-frequency magnetic field around the rail, offering a potential energy source for self-powered trackside monitoring nodes. The H-shaped-core magnetic field energy harvester (MFEH) is attractive because it can be installed beneath the rail without enclosing [...] Read more.

During train operation, railway traction-returning current generates a power-frequency magnetic field around the rail, offering a potential energy source for self-powered trackside monitoring nodes. The H-shaped-core magnetic field energy harvester (MFEH) is attractive because it can be installed beneath the rail without enclosing the conductor, yet its output is strongly affected by the coupled rail-core-coil system. To clarify these effects, a three-dimensional electromagnetic-circuit-coupled finite-element model of an experimentally validated laminated-silicon-steel H-shaped-core MFEH was established to examine core and coil parameters. Increasing the center-leg and side-leg lengths weakens demagnetization but intensifies eddy-current losses, causing output power to approach saturation. Under a 50 Hz, 300 A current in a 54E1 rail and series-tuned matching, output power approaches 5.1 W beyond a center-leg length of 1000 mm and 3.25 W beyond a side-leg length of 700 mm. Within the investigated ranges, center-leg and side-leg lengths of approximately 800 and 400 mm provide the best power–volume performance, respectively. Increasing side-leg height or width also improves output. A larger coil span improves output by reducing internal resistance, whereas more turns yield diminishing gains because of higher winding and eddy-current losses. These findings provide a quantitative basis for parametric design of H-shaped-core MFEHs in railway environments. Full article

(This article belongs to the Section Vehicle Engineering)

24 pages, 6940 KB

Open AccessArticle

A Rapid-Mode-Switching Small-Scale Crawling–Jumping Robot Based on a Dual-Clutch Mechanism

by Quan Liu, Beibei Dong, Mingge Li, Wenxi Zhang and Zhongjun Yin

Machines 2026, 14(7), 745; https://doi.org/10.3390/machines14070745 - 2 Jul 2026

Abstract

Conventional crawling–jumping robots often suffer from slow mode transitions and poor motion continuity, limiting their adaptability in complex environments. This paper presents a compact hybrid robot featuring a novel dual-clutch mechanism composed of an actively driven clutch and a passive clutch, enabling rapid, [...] Read more.

Conventional crawling–jumping robots often suffer from slow mode transitions and poor motion continuity, limiting their adaptability in complex environments. This paper presents a compact hybrid robot featuring a novel dual-clutch mechanism composed of an actively driven clutch and a passive clutch, enabling rapid, seamless transitions between crawling and jumping. The active clutch, driven by a single motor, controls elastic energy storage and release. In contrast, the passive clutch mechanically decouples the jumping and crawling modules during energy storage and enables automatic reset within 25 ms after takeoff. A dynamic jumping model and trajectory adjustment method are established, and analytical crossing criteria for trenches, obstacles, and platforms are derived. Experiments show that the robot achieves less than 3 s transition time from jumping to crawling, exhibits linearly controllable jumping height, and can actively regulate jump trajectories by independently adjusting crawling speed and takeoff velocity. The robot successfully traverses obstacles up to 2.7 times its body length. The proposed design provides an effective solution for rapid transitions between multimodal locomotion in small-scale robots. Full article

(This article belongs to the Special Issue Advances in Robot Kinematics and Dynamics: Innovations, Control Strategies, and Practical Applications)

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20 pages, 9321 KB

Open AccessArticle

Adaptive Load Balancing for Efficient Background Subtraction in Intelligent Transport Systems on Low-Cost Embedded Platforms

by Brahim Tebbaa, Mohamed Ragoubi, Lhoussain El Hajjami, Assia Arsalane, Abdessamad Klilou and Vidas Žuraulis

Machines 2026, 14(7), 744; https://doi.org/10.3390/machines14070744 - 2 Jul 2026

Abstract

Background subtraction (BS) is a fundamental technique in intelligent transport vision systems, widely used to detect and track moving objects, such as vehicles, pedestrians and obstacles, in driving environments. It plays a crucial role in advanced driver-assistance systems (ADAS) and autonomous driving by [...] Read more.

Background subtraction (BS) is a fundamental technique in intelligent transport vision systems, widely used to detect and track moving objects, such as vehicles, pedestrians and obstacles, in driving environments. It plays a crucial role in advanced driver-assistance systems (ADAS) and autonomous driving by enabling scene understanding and real-time motion analysis. However, BS processing must be optimized when targeting real-time processing on resource-constrained embedded systems, which present significant challenges due to limited computational power, memory constraints, and strict real-time requirements. Among the most commonly used BS techniques, the Codebook model and Gaussian Mixture Models (GMM) are known for their higher accuracy and light-model compared to many deep learning-based BS. In this work, we propose a fully heterogeneous CPU and GPU parallel implementation of both Codebook and GMM algorithms with an auto-load balancing over the processing units. This approach has been evaluated on the low-cost Jetson Orin Nano platform from NVIDIA, enabling efficient workload balancing across heterogeneous hardware resources. The suggested solution yields significant performance improvements over the state-of-the-art, achieving 59 frames per second (FPS) for GMM and 66 FPS for the Codebook method on full-HD (1080p) video streams. The results confirm the effectiveness of the proposed method in accelerating BS and demonstrate its suitability for real-time deployment in resource-constrained embedded environments. Full article

(This article belongs to the Special Issue Trends and Challenges in the Development and Operation of Modern Vehicles)

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15 pages, 4656 KB

Open AccessArticle

Torque Ripple Reduction and Power Density Improvement of a Slotless Motor Design for Rack-Type Electrical Power Steering

by Dong-Youn Shin, Do-Hyeon Choi, Hyung-Sub Han, Deepak Dubal and Won-Ho Kim

Machines 2026, 14(7), 743; https://doi.org/10.3390/machines14070743 - 2 Jul 2026

Abstract

Electric power steering (EPS) systems have become the dominant steering solution in modern vehicles due to their advantages in energy efficiency and driving convenience. Among EPS configurations, rack-type EPS(R-EPS) is widely adopted in mid-to-large vehicles for its direct and responsive steering characteristics. However, [...] Read more.

Electric power steering (EPS) systems have become the dominant steering solution in modern vehicles due to their advantages in energy efficiency and driving convenience. Among EPS configurations, rack-type EPS(R-EPS) is widely adopted in mid-to-large vehicles for its direct and responsive steering characteristics. However, conventional slotted motors used in R-EPS suffer from cogging torque and torque ripple caused by periodic reluctance variation in the stator tooth structure, resulting in vibration and noise directly perceived by the driver. This article proposes a slotless Surface Permanent Magnet Synchronous Motor (SPMSM) employing a Bar-Type magnet with a double-bridge rotor structure for R-EPS applications. By eliminating stator teeth, the proposed design achieves a uniform reluctance distribution during rotor rotation, theoretically reducing cogging torque to zero. The absence of stator tooth magnetic saturation further enables the use of high-remanence permanent magnets, improving gravimetric power density and enabling motor miniaturization. The proposed motor was designed and verified through finite element analysis (FEA). Compared to the conventional Arc-Type motor, the optimized design achieves a torque ripple reduction of 64.7% (from 5.07% to 1.79%) through Bar-Type magnet shaping combined with a 5 mm edge filet. The optimized design achieved an output power of 359.9 W, a gravimetric power density of 276.85 W/kg based on an active part weight of 1.30 kg, and a structural safety factor of 1146, demonstrating the effectiveness of the proposed double-bridge slotless motor for R-EPS applications. Full article

(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)

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24 pages, 16900 KB

Open AccessArticle

The Design and Development of a Temperature Control System for Powder Sintering Processes in a Ceramic Sintering Machine

by Waldemar Samociuk

Machines 2026, 14(7), 742; https://doi.org/10.3390/machines14070742 - 1 Jul 2026

Abstract

This paper presents a control system for a powder sintering machine utilizing the alternating current (AC) sintering technique assisted by uniaxial compression. The AC flows simultaneously through both the sintered material and the die assembly. The procedural model of the control system and [...] Read more.

This paper presents a control system for a powder sintering machine utilizing the alternating current (AC) sintering technique assisted by uniaxial compression. The AC flows simultaneously through both the sintered material and the die assembly. The procedural model of the control system and its functionalities are described. Thermal models of the die assembly were developed using First-Order Plus Dead-Time (FOPDT) and Second-Order Plus Dead-Time (SOPDT) frameworks, followed by the synthesis of the control system. A prototype machine, developed at the University of Radom in cooperation with the University of Life Sciences in Lublin, is discussed, including its control algorithm and key process parameters affecting sample properties, established based on literature data. To achieve temperatures of 1700 °C and above, a specialized high-power supply (180 A, 400 VAC) with phase-angle and integral-cycle (group) control was implemented. The control system is based on a PLC equipped with temperature control loops, alongside measurements of shrinkage (displacement), compaction force, and auxiliary signals (vacuum and cooling systems). A dedicated control algorithm was developed to ensure process repeatability and optimization. For the ZrO₂-based composite analyzed in this manuscript, an FOPDT process model was determined, yielding an equivalent transport delay of approximately 83 s and an equivalent time constant of nearly 333 s. Based on this model, PID controller parameters were derived, and a time-profile program control algorithm was developed. Furthermore, based on literature data, selected mechanical properties of composites obtained via AC sintering are presented, which served as foundational design criteria for the developed machine. Full article

(This article belongs to the Special Issue Dimensional Measurement, Analysis, and Control for Precision Manufacturing)

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