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        <title>Frontiers in Mechanical Engineering | Mechatronics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/mechanical-engineering/sections/mechatronics</link>
        <description>RSS Feed for Mechatronics section in the Frontiers in Mechanical Engineering journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-07T22:45:50.374+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1733754</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1733754</link>
        <title><![CDATA[Mechanical processing production management technology based on event scheduling and digital management system]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tianshu Huo</author>
        <description><![CDATA[IntroductionMechanical processing production management plays a critical role in optimizing production efficiency and ensuring product quality. Traditional management methods face challenges such as equipment failures, insufficient flexibility in resource scheduling, and low production efficiency. This study proposes a mechanical processing production management technology based on event scheduling and a digital management system to improve production efficiency and order qualification rate while reducing costs.MethodsA digital management system integrating IoT and data analysis technologies was developed to enable real-time monitoring and management of the production process. A multi-objective event scheduling method incorporating the Whale Optimization Algorithm-Grey Wolf Optimizer (WOA-GWO) was adopted to optimize production scheduling. The system employs an event-driven mechanism to capture production line events in real time and dynamically adjust resource allocation and production plans.ResultsOn the Industrial Internet of Things Simulation Dataset (IIoTSD), the recognition accuracy of the system stabilized at around 98%. On the Mechanical Processing Production Historical Dataset (MPPHD), accuracy stabilized at approximately 95%. In practical enterprise applications, the resource utilization rate remained above 90%, and the production cost stayed below CNY 100,000 by the 500th batch. The order qualification rate was maintained at around 98%, and production efficiency remained at approximately 0.95.DiscussionThe proposed approach effectively enhances the level of automation and intelligence in mechanical processing production lines, strengthening the market competitiveness of enterprises. The system demonstrates superior performance in event recognition, resource scheduling, and cost control, providing an intelligent solution for production management. Future work will focus on improving system resilience against external disruptions, enhancing algorithm generalizability, and developing lightweight deployment solutions for small and medium-sized enterprises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1780107</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1780107</link>
        <title><![CDATA[Design and testing of an intelligent anti-runaway rail-shoe control system]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jun Shi</author><author>Zongfang Zhang</author>
        <description><![CDATA[Conventional anti-runaway rail shoes (wheel chocks) used for rail vehicles are difficult to monitor in real time, labor-intensive to operate, and prone to operational errors such as incorrect placement/removal and missed placement/removal. To address these limitations, this paper presents an intelligent anti-runaway rail-shoe control system. A visual monitoring platform is installed in the locomotive cab and in the control rooms of marshalling yards and section stations, providing real-time status indication for rail-shoe management. Compared with traditional rail shoes, the proposed system wirelessly transmits key state information—such as on-rail status, wheel-contact (loaded) status, and lock status—to the monitoring platform. In addition, an active light-emitting alarm helps operators quickly locate installed shoes, thereby reducing the likelihood of missed placement/removal. Functional tests were conducted with reference to railway rail-shoe management requirements and representative field operating scenarios. The results demonstrate that the proposed system meets the requirements for on-site railway anti-runaway operations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1775579</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1775579</link>
        <title><![CDATA[VSG control of photovoltaic energy storage grid-connected system based on improved SMA]]></title>
        <pubdate>2026-03-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ping Wu</author>
        <description><![CDATA[IntroductionVirtual synchronous generator (VSG) control techniques face limited adaptability in modern grid-connected systems. This study aims to enhance the adaptability and performance of VSG control by developing an optimized photovoltaic-storage grid-connected system.MethodsA proportional-derivative controller was incorporated into the photovoltaic-storage grid-connected system. An improved slime mould algorithm (SMA) was introduced to optimize the inertia and damping coefficients of the VSG. The proposed method was evaluated through experimental testing, simulation analysis, and dynamic response assessment under varying illumination conditions.ResultsThe experimental results demonstrated that the accuracy of the proposed improved SMA reached 97.87%, with a recall of 98.58%, both superior to the comparison algorithms. Simulation analysis indicated that the improved SMA effectively suppressed power overshoot to only 1.48 kW, lower than the comparison algorithms. Furthermore, the dynamic response testing showed that the goodness-of-fit coefficient for finding optimal parameters under different illumination conditions was 0.983, significantly higher than the comparison algorithm.DiscussionThese findings demonstrate that the improved SMA is effective for optimizing VSG control parameters and possesses practical value for enhancing the stability and adaptability of photovoltaic-storage grid-connected systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1687945</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1687945</link>
        <title><![CDATA[Design, testing, and dimensional optimization of a biomimetic microspine gripper based on feline paw structure]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qingpeng Wen</author><author>Yuepeng Zhang</author><author>Xianfeng Wu</author><author>Xuansheng Wang</author><author>Linzhong Xia</author><author>Changwei Lv</author>
        <description><![CDATA[BackgroundMicrospine grippers are critical for wall-climbing robots and drones to attach to rough surfaces in complex environments. Existing designs focus on structural practicality but overlook rational dimensional optimization and accurate modeling of adhesion force. One-degree-of-freedom (1-DOF) grippers also exhibit poor collision resistance and low grasping stability on variable-roughness surfaces.MethodsTo address these shortcomings, a two-degree-of-freedom (2-DOF) biomimetic microspine gripper inspired by the retractable claw structure of feline paws is proposed. A microspine stiffness model based on Castigliano’s Second Theorem, combined with a rough surface model and a gripper statics model, is established to quantify adhesion force. The performance atlas method combined with dimensionless parameter processing is adopted for dimensional optimization, where the top 25% of adhesion force data in each subplot is defined as the high-performance region to identify the optimal parameter combination.ResultsExperimental validation shows the stiffness model has a corrected relative error of only 4.5%. The optimized gripper achieves stable adhesion of 30 N and 22.7 N on rough asphalt and smooth stone surfaces, respectively, with a 95% grasping success rate on variable-roughness surfaces. Compared with our self-developed 1-DOF gripper, the proposed design effectively reduces collision-induced microspine damage and significantly improves grasping stability and environmental adaptability.ConclusionThis work provides a theoretical and optimization framework for microspine gripper design, and the biomimetic design strategy offers new insights for the development of high-performance robotic attachment components.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1778543</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1778543</link>
        <title><![CDATA[Numerical investigation of pressure-equalizing groove configuration effects on gas bearing performance]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yang Su</author><author>Fangjian Wan</author><author>Lifang Wang</author><author>Hang Xiu</author><author>Qi Qin</author>
        <description><![CDATA[High-speed rotating machinery has extremely high requirements for the performance and reliability of aerostatic bearings, and the design of pressure-equalizing groove structure is a key factor determining the performance of aerostatic bearings under high-speed conditions. At present, research on the influence of pressure-equalizing groove structure on the performance of high-speed aerostatic bearings is still relatively limited. In this study, aerostatic thrust gas bearings with three pressure-equalizing groove structures (rectangular, fan-shaped and drop-shaped) were designed, and their performance characteristics were comprehensively analyzed by numerical simulation method. A CFD model of the bearing was established based on the Navier-Stokes equations and the model accuracy was verified. The performance of bearings with each structure under different working conditions of air film thickness, supply pressure and rotational speed was explored. The study found that the shape of pressure-equalizing groove has a significant impact on the load-carrying capacity, pressure distribution, stiffness and stability of the bearing. Under high-speed conditions, vortices in the groove critically affect the pressure distribution in the high-pressure zone of the bearing, which in turn determines the load-carrying capacity. Fan-shaped and drop-shaped grooves can effectively suppress vortices due to their divergent structures, and their load-carrying capacity and stiffness during high-speed operation are superior to those of rectangular grooves, while rectangular and fan-shaped grooves have smaller pressure fluctuations and exhibit better stability. There is a clear correlation mechanism between the divergent characteristics of different pressure-equalizing grooves and vortex suppression. Fan-shaped and drop-shaped grooves can promote the expansion of the high-pressure area of the air film and enhance the hydrodynamic effect, while the complete vortex in the rectangular groove limits the development of the high-pressure area. The research results provide theoretical support for the design and optimization of aerostatic bearings, and contribute to the research and development of high-performance bearings adapted to high-speed rotating machinery.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1778120</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1778120</link>
        <title><![CDATA[Integrated energy optimization for metal waste cleaning-24 robot in local manufacturing based on multi-objective approach]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andi Amijoyo Mochtar</author><author>La Ode Muhammad Ali</author>
        <description><![CDATA[Modern manufacturing industries face increasing pressure to enhance operational efficiency while reducing energy costs and environmental impact. This research develops a metal waste cleaning robot with integrated multi-objective energy optimization for local manufacturing applications. The robot integrates 28 main components including dual motor systems (80 W drive motor, 60 W arm motor), HC-SR04 ultrasonic sensor, ESP32 microcontroller, and hierarchical thermal protection. Non-dominated Sorting Genetic Algorithm II (NSGA-II) simultaneously optimizes energy consumption, coverage completeness, and operational time. The multi-objective optimization framework achieves significant energy reductions through three independent mechanisms: trajectory planning optimization reduces total energy consumption by 30% (from 235.7 Wh to 165 Wh per cycle), adaptive control systems reduce motor power consumption by 50% (from 280 W to 140 W) through dynamic voltage adjustment based on environmental complexity, and strategic base station placement reduces travel distance by 20% (from 150 m to 120 m per cycle), resulting in corresponding energy savings. ANSYS validation confirms structural stability with maximum equivalent elastic strain of 7.6839 × 10−5 m/m and maximum equivalent deformation of 6.710 × 10−5 m (67.10 μm) under operational loading, demonstrating that the structure operates well within the elastic limit with safety factor >5. The robot demonstrates total power consumption of 165 W with 75.4% cleaning efficiency, reducing operational time from 35 min (manual methods) to 8.4 min across four material types (aluminum, copper, steel, glass). Performance testing shows 76.7% efficiency for chip cleaning (7 min) and 87.5% efficiency for metal dust cleaning (5 min). The hierarchical thermal protection system ensures operational safety with motor temperature sensors providing 35% protection effectiveness. This integrated optimization framework provides validated solutions for local manufacturing industries with limited technology accessibility, contributing to sustainable energy-efficient industrial robot for metal waste management in developing countries.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1770664</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1770664</link>
        <title><![CDATA[Fault diagnosis method for high-voltage circuit breakers based on physics-informed transfer learning]]></title>
        <pubdate>2026-03-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dong Wang</author><author>Lubo Zhou</author><author>Liyun Xie</author><author>Xipu Liu</author><author>Shiqi Dong</author><author>Junhua Liu</author>
        <description><![CDATA[IntroductionHigh-voltage circuit breakers are core control and protection equipment in power systems, and their operational status directly affects device stability and power grid security. Improving the accuracy of their fault detection is a key demand for the operation and maintenance of power equipment.MethodsThis study proposes a fault detection method for high-voltage circuit breakers based on multi-source information and motion analysis. First, a 1-dimensional recurrent neural network (1DRNN) is used to analyze voiceprint and current signals to extract feature data related to the mechanical state of the operating mechanism. Second, a physics-informed transfer learning network model consisting of a Common Feature Learning Network (CFLN) and a Mechanical Feature Learning Network (MFLN) is constructed to explore shared features between multi-source signals and mechanical parameters and extract specific features of individual mechanical parameters in a targeted manner. Meanwhile, a multi-head attention mechanism is integrated to enhance the model’s ability to capture key features, and a physics-based loss function is designed to improve the physical consistency of the model during mechanical parameter identification.ResultsExperimental verification shows that the proposed method achieves a fault diagnosis accuracy of over 93% for high-voltage circuit breakers, and the model can still maintain high diagnostic stability and detection accuracy under noise interference conditions.DiscussionThrough the design of deep fusion of multi-source signals and embedding of physical information, this method makes up for the information defects of single-signal diagnosis, solves the problem of lack of physical consistency in data-driven models, and improves the environmental adaptability of fault diagnosis models, providing a practical technical solution for the intelligent fault diagnosis of high-voltage circuit breakers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1766169</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1766169</link>
        <title><![CDATA[Strength optimization and assembly technology of key parts of reducer based on parametric design]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fengchan Zhou</author><author>Xianqiao Zhao</author><author>Wei Guo</author>
        <description><![CDATA[IntroductionWith the development of high efficiency in the machinery industry, the importance of reducers as key transmission components has become increasingly prominent.MethodsThis study proposes a strength optimization and assembly technology improvement plan for key parts of the reducer based on parametric design.ResultsThe outcomes revealed that compared with the unmodified control group, the transmission error of experimental group B (moderate modification) was 25.8%–32.4% lower than that of the control group under the working condition of 1500r/min+150N·m, and the noise fluctuation range was only 0.1 dB. In the 800 h continuous operation experiment, the contact stress under the 500r/min and 1000r/min operating conditions was reduced by 3.6% and 4.8% respectively compared with the control group. In the 1050 h environmental adaptability test, the meshing stiffness drop of the optimized design was 2%–5% lower than that of the traditional design, and the increase in transmission error was significantly smaller.DiscussionCompared with existing research, the parametric design method proposed in this study achieves part strength balance through parameter sensitivity and coupling weight analysis, combined with tooth profile modification and natural frequency adjustment. It not only optimizes part performance, but also has significant advantages in noise reduction, deformation control, and design efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1777195</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1777195</link>
        <title><![CDATA[Optimization of time-varying load dynamic response of industrial robot PLC system based on improved model reference adaptive control]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jing Li</author><author>Aoqi Lian</author><author>Jiawei Yang</author><author>Lihua Liu</author>
        <description><![CDATA[IntroductionIn the process of industrial automation, industrial robots are widely used in complex operations such as welding, assembly, and handling. The dynamic response performance under time-varying load conditions directly affects production efficiency and control quality.MethodsTo improve the dynamic response speed and control accuracy of industrial robot Programmable Logic Control (PLC) systems under time-varying loads, an improved Model Reference Adaptive Control (MRAC) strategy that combines Fuzzy Correction Adaptive Law (FCAL) and Particle Swarm Optimization (PSO) algorithm is designed. It combines an Extended Kalman Filter (EKF) load observer with a composite control law to adapt to the discrete characteristics of PLC and optimize multi-task scheduling.ResultsExperiments show that in three scenarios: automotive welding, electronic assembly, and metal cutting, the production efficiency of this system is increased by 20.7%–23.8% compared with traditional PLC methods, and the dynamic response time is shortened from 0.8 s to 0.3 s. The product qualification rate increases from 1.9% to 3.9%, and the positioning error of the assembly robot drops from ±0.1 m to ±0.05 m. The torque fluctuation of the cutting robot motor drops from 1.0 N m to 0.58 N m, the load observation error does not exceed 0.05 N m, and the angular velocity overshoot is less than 1.2%. DiscussionThrough the deep integration of adaptive control strategy and PLC system, the dynamic response speed and control accuracy of industrial robots under time‐varying load conditions are effectively improved, and production efficiency, product qualification rate, and energy consumption indicators are improved. This study provides reliable technical support for the field of flexible manufacturing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1764606</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1764606</link>
        <title><![CDATA[Multi-state robot posture detection method based on joint optimization of target-key point detection]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoli Zhang</author>
        <description><![CDATA[IntroductionIndustrial robots are the core equipment in intelligent manufacturing. The accuracy of their posture detection directly determines their high-precision operational capabilities. It is necessary to address the problems existing in the current visual detection methods, such as focusing only on the state of a single robot, difficulty in balancing real-time performance and accuracy, and the unbalanced optimization of multi-joint angle errors.MethodsA multi-state robot posture detection method based on joint optimization of targets and key points has been proposed. This method employs an improved cross-stage local network as the backbone network, simultaneously outputting the features of target detection and key point detection, and achieving the collaborative optimization of dual tasks through weighted joint loss. Additionally, a multi-state adaptive module is designed, which determines the state based on the motion vectors of adjacent frames, compensates for motion blur using directionally fast rotation binary robust independent basic feature matching, and corrects the perspective deviation using posture correction parameters.ResultsThe results show that when the intersection and union threshold of the introduced model ranges from 0.5 to 0.95, the average target detection accuracy is 94.73%; when the key point detection threshold is 0.2, the key point detection accuracy rate is 98.7%; the average absolute errors of joint angles in the fixed, moving and unknown states are 7.2°, 8.1° and 9.5° respectively, and the inference speed is only 43 frames per second.DiscussionThis method can enhance the detection performance in multi-state scenarios and provide technical support for the complex operations of industrial robots.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1744710</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1744710</link>
        <title><![CDATA[Research on intelligent diagnosis of mechanical rolling bearing faults through transfer learning]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yougang Zhang</author>
        <description><![CDATA[IntroductionThis article proposes a fault diagnosis algorithm for mechanical rolling bearings based on transfer learning.MethodsThe proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability distribution difference module, thereby achieving transfer learning between source domain samples and target domain samples. Simulation experiments were performed. On a PT100 bearing fault simulation test platform, vibration signals of bearings were collected in cases of normal operation, inner race faults, outer race faults, and ball faults at motor speeds of 1,000, 1,500, and 2,000 r/min. The diagnostic performance of support vector machine (SVM), back-propagation neural network (BPNN), and the proposed algorithm was evaluated in operating condition transfer tasks. Moreover, ablation experiments were conducted.ResultsIt was found that the proposed algorithm could effectively and accurately identify bearing faults in the face of changes in operating conditions.DiscussionBoth the domain category judgment module and the inter-domain conditional probability distribution difference could effectively achieve transfer learning of the diagnostic model.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1750884</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1750884</link>
        <title><![CDATA[Hybrid fuzzy-SVM collaborative reasoning framework for intelligent CNC turning process planning]]></title>
        <pubdate>2026-02-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huaying Qiao</author><author>Rizauddin Ramli</author><author>Liancheng Zheng</author><author>Jiutao Zhao</author>
        <description><![CDATA[IntroductionThe optimization of machining process decision-making remains a major challenge in intelligent manufacturing due to the uncertainty of process information, incompleteness of rule bases, and the tendency of traditional algorithms to converge to local optima. Therefore, enhancing the adaptability and robustness of decision-making systems is a crucial task for achieving efficient and reliable computer numerical control (CNC) process planning.MethodsThis study proposes a hybrid decision-making approach that integrates fuzzy theory with support vector machines (SVM) to address uncertainty and incomplete knowledge representation in CNC turning. An Analytic Hierarchy Process (AHP) is used to determine the relative importance of influencing factors, and trapezoidal membership functions are designed to determine the credibility of fuzzy reasoning rules. When the credibility value falls below a defined threshold, a linear-kernel SVM model is activated to provide alternative decisions which formed a fuzzy-SVM collaborative reasoning mechanism.ResultsExperimental validation demonstrates that the proposed hybrid fuzzy-SVM collaborative method achieves remarkable classification accuracy on the test dataset. The system maintains stable performance even under low-credibility or incomplete rule conditions. The SVM module effectively compensates for the limitations of the fuzzy reasoning process, thereby improving the robustness of decision inference compared to single-model approaches.ConclusionThe proposed fuzzy-SVM collaborative reasoning framework enhances the adaptability, stability, and interpretability of CNC machining process decision-making. These findings offer a practical and scalable solution for intelligent process planning in complex and uncertain manufacturing environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1741396</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1741396</link>
        <title><![CDATA[Harvesting target positioning and robotic arm obstacle avoidance algorithm based on improved YOLOv8 and BIT*]]></title>
        <pubdate>2026-01-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yingwu Xu</author>
        <description><![CDATA[IntroductionTo address the core challenges of inaccurate fruit occlusion localization and inefficient robotic arm dynamic obstacle avoidance in complex, unstructured agricultural environments, this study proposes an integrated algorithm for harvesting.MethodsThe proposed algorithm is built upon an improved YOLOv8 model and the BIT* planner. The YOLOv8 model was enhanced by introducing the Swin Transformer module to improve multi-scale feature fusion and global context modeling. The BIT* planner was integrated with a BiLSTM network to endow it with dynamic obstacle prediction capabilities, thereby constructing a unified architecture for visual perception and motion planning.ResultsExperimental results demonstrated that the algorithm achieved real-time performance with a processing frame rate of 32.7 fps and an inference time of 32.6 ms for target localization, with a localization error standard deviation as low as 1.70 mm. In obstacle avoidance planning, it achieved a balance with manipulator energy consumption of 124.58 J, while controlling the computational load and memory resource consumption per task to 22.7 GFlops and 187 MB, respectively.DiscussionThis approach provides a high-precision, low-energy-consumption cooperative control solution for agricultural harvesting robots, advancing the practical application of automated fruit and vegetable harvesting.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1736300</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1736300</link>
        <title><![CDATA[Multi-objective optimization of beam transport in medical heavy ion accelerators using an improved non-dominated sorting differential evolution algorithm (NSDE)]]></title>
        <pubdate>2026-01-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yanhong Yang</author><author>Mian Zhang</author><author>Kun Wei</author>
        <description><![CDATA[To address the issues of high-dimensional coupling parameters easily falling into local optima and multi-objective conflicts in the beam transport of medical heavy ion accelerators, this paper proposes an improved non-dominated sorting differential evolution (NSDE) algorithm. The algorithm employs inverse learning for initialization and introduces an adaptive mechanism to adjust the mutation factor and crossover probability online, balancing exploration and exploitation. Additionally, it incorporates local enhancement based on crowding distance in particle swarm optimization (PSO) to refine non-dominated elite solutions. Large-scale experiments based on FLUKA Monte Carlo coupled simulation (nine-dimensional decision variables) have shown that the improved NSDE has increased the beam transport efficiency from the baseline of 92.42% to 99.21% (an improvement of 6.79%), while also achieving continuous improvements in key physical indicators such as the beam spot size at the end point, system power consumption, and energy retention rate. The research indicates that the proposed method exhibits significant advantages in enhancing optimization quality and maintaining robustness, making it suitable for accelerator engineering optimization that demands stringent real-time performance and multi-objective accuracy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1759452</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1759452</link>
        <title><![CDATA[Online fault compensation control method for ROV based on decoupling algorithm]]></title>
        <pubdate>2026-01-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nan Zhang</author>
        <description><![CDATA[IntroductionWith the increasing complexity of underwater operations, remotely operated vehicles systems face the dual challenges of multi-source interference and component failures in unknown environments.MethodsTo achieve high-precision control of remotely operated vehicles arms under fault conditions, this paper proposes an online fault compensation control method based on a decoupling algorithm. This method separates the end-effector position and attitude control of the master and slave arms through a pose decoupling algorithm, constructs an observer-based fault diagnosis mechanism, and combines H∞ robust control and online adaptive strategies to achieve dynamic compensation for combined sensor and thruster faults.ResultsThe results show that in dual-arm cooperative operation, the spatial trajectory tracking deviation of the robotic arm can be controlled within 4.3 mm, with a maximum deviation of 2.643 mm in the X-axis direction and a planning deviation of 3.075 mm in the Y-axis direction. Compared with backstepping fault-tolerant control and power sliding mode control, the method used in this study has a maximum deviation of only 0.01° in yaw angle control, a position control error reduced to 1.2 mm, and a maximum trajectory tracking error of 2.1 mm, which is significantly better than the comparative methods. Furthermore, the system can rapidly approach the desired posture within 50 seconds and maintains stable operation under various fault scenarios.DiscussionThis demonstrates that the proposed method can effectively improve the operational accuracy and fault “tolerance of remotely operated vehicles in complex environments, providing a new technology for solving the control problems of robot systems under fault conditions.”]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1754564</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1754564</link>
        <title><![CDATA[Fault diagnosis of electromechanical systems considering noise suppression and multiscale signal features]]></title>
        <pubdate>2026-01-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoqiao Qi</author><author>Yuance Yang</author><author>Shukui Han</author><author>Guangyu Bai</author><author>Nanxiang Fang</author>
        <description><![CDATA[IntroductionIn the electromechanical system, the performance of a direct current brushless motor is determined by its rolling bearings, which play a decisive role in ensuring the safe and smooth operation of the entire system. Thus, fault diagnosis of these bearings is of paramount importance. However, existing methods for diagnosing faults often suffer from low accuracy, particularly under complex noise conditions.MethodsThis study proposes an innovative approach to fault diagnosis that enhances the accuracy and robustness of detecting faults in brushless direct current motor rolling bearings. To achieve this goal, this study first employs wavelet threshold denoising to suppress noise in motor current signals and performs multiscale feature fusion. Additionally, a fault diagnosis method is developed by integrating a convolutional attention mechanism.ResultsThe outcomes indicated that the proposed diagnostic method achieved a recall rate of 90.89% and a precision rate of 98.69%, both higher than those of the comparative methods. The suggested approach outperformed the comparison methods in all four fault categories, with diagnostic accuracy rates of 99.4%, 98.9%, 98.8%, and 99.3%.DiscussionThe findings of the experiments reveal that the proposed diagnostic method can effectively identify faults in rolling bearings of brushless direct current motors, providing a theoretical foundation for research in the field of electromechanical system fault diagnosis. The contributions of this research are in three aspects. First, the BLDCM rolling bearing current signal is reconstructed using a multiscale feature and wavelet threshold denoising. This significantly improves the signal quality and ability to extract fault features. Second, CBAM, residual network and Swin Transformer encoder are integrated into the fault diagnosis model. Compared with the existing methods, higher accuracy and precision are achieved. This study finally provides a solid theoretical foundation for further research in the field of electromechanical system fault diagnosis, particularly for BLDCM rolling bearing fault diagnosis under complex noise conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1728504</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1728504</link>
        <title><![CDATA[Valley-protected topological interface states in metastructures with internal geometric rotation]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Wang</author><author>Shuang Yu</author><author>Chengcan Jiang</author><author>Jing Hu</author>
        <description><![CDATA[Topological mechanical metamaterials have garnered significant attention for their ability to exhibit robust, defect-immune wave propagation and mechanical behaviors, inspired by topological protection mechanisms in condensed matter physics. In this study, we present a novel mechanical metamaterial design that introduces rotational geometric parameters within the unit cell to explicitly break spatial inversion symmetry. By precisely tuning the rotation angle of structural elements in the unit cell, we induce asymmetric valley states with opposite Berry curvatures, thereby realizing the valley Hall effect in a mechanical framework. This purely geometric approach avoids reliance on material composition gradients or external fields, offering intrinsic control over valley polarization through structural design alone. Numerical simulations and mechanical analyses demonstrate that the proposed metamaterial supports topologically protected interface states at the boundary between regions of distinct valley topologies. These interface states exhibit unidirectional propagation, confirming their topological protection. This work provides a universal geometric strategy to engineer topological phenomena in structural systems. The realized topologically protected interface states hold promise for applications in high-precision sensors, energy harvesting devices, and vibration isolation systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1713677</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1713677</link>
        <title><![CDATA[Design and optimization of wireless sensor system for self-powered crawler crane based on piezoelectric energy harvesting]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yufang Sun</author><author>Huiqian Liu</author>
        <description><![CDATA[IntroductionCrawler cranes are widely used in large–scale infrastructure construction, where structural health monitoring is essential to ensure operational safety. Wireless sensor networks have become a mainstream solution for crane monitoring; however, conventional battery–powered systems suffer from frequent replacement, complex wiring, and limited service life, which restrict long–term deployment.MethodsTo address these issues, a piezoelectric energy harvesting electromechanical coupling model tailored to crawler crane operating conditions is developed. Furthermore, a low–power wireless communication protocol incorporating cluster–head data aggregation and dynamic duty–cycle adjustment is introduced, enabling deep collaboration between energy harvesting, energy storage, and wireless sensing modules.ResultsSimulation results show that within the resonance frequency range of 30–35 Hz, the optimized piezoelectric energy harvesting module achieves a peak output power of 8.0 mW at an acceleration of 0.5 g, representing a 47.5% improvement over the unoptimized configuration. Under the same excitation level, the energy storage capacitor voltage increases to 3.0 V within 25 s. Field deployment experiments involving six sensor nodes demonstrate that the proposed joint optimization scheme attains an energy utilization rate of 81.5%, while extending the average node lifetime to 397.4 h, which is 65.6% longer than that of the unoptimized scheme.DiscussionThis study proposes a “structure–circuit–communication” collaborative optimization framework for complex vibration environments of crawler cranes. The proposed approach enables long‐term online monitoring of wireless sensor nodes without batteries and provides a feasible technical pathway for upgrading self-powered Internet of Things systems in large‐scale construction machinery.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1687802</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1687802</link>
        <title><![CDATA[Research on fault diagnosis in the operation monitoring of permanent magnet synchronous motors through deep learning]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhidong Guo</author><author>Xiaobei Pan</author>
        <description><![CDATA[BackgroundPermanent magnet synchronous motor (PMSM) may develop faults during long-term operation, affecting the stability and safety of the drive system.ObjectiveThis paper aims to identify the types of PMSM operation faults using a deep learning algorithm.MethodsThe convolutional neural network (CNN)-gated recurrent unit (GRU) algorithm was compared with the support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN) algorithms. Ablation experiments were conducted. Finally, the Shapley additive explanations algorithm was used to calculate the importance of feature indicators.ResultsThe CNN-GRU algorithm had better fault-diagnosis performance compared with the other three algorithms and was easier to make an accurate diagnosis of inter-turn short-circuit faults in stator windings. The precision, recall rate, and F-score of the CNN-GRU algorithm were 0.950, 0.948, and 0.949, respectively; the corresponding values of the BPNN algorithm were 0.823, 0.819, and 0.821, respectively; the corresponding values of the RF algorithm were 0.719, 0.713, and 0.716, respectively; the corresponding values of the SVM algorithm were 0.707, 0.700, and 0.703, respectively. Ablation experiments verified the effectiveness of the CNN and GRU algorithms for the entire algorithm. Stator current and voltage were of the highest importance in the fault diagnosis model, followed by motor torque, and motor temperature was least important.ContributionThe contribution of this paper lies in improving the recognition performance of fault types by combining two intelligent algorithms, CNN and GRU, and taking into account both local features and time-series features. It provides an effective reference for ensuring the stable operation of motor drive systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1715466</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1715466</link>
        <title><![CDATA[Motor automation speed regulation method with sliding mode control and adaptive gain]]></title>
        <pubdate>2026-01-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu Zhang</author><author>Yuping Li</author>
        <description><![CDATA[IntroductionMotor speed control is crucial for maintaining the normal operation of motors. In view of the limitations of current motor speed control methods, such as high parameter dependence, obvious control signal buffering, and low flexibility, an automatic speed control model combining Sliding Mode Control and adaptive gain is proposed.MethodsThis model combines adaptive gain with a Sliding Mode Control to design an Adaptive Sliding Mode Control for motor speed control. Then, the super helix algorithm is used to adjust the sliding mode gain coefficient to suppress the controller’s buffing problem. At the same time, an evaporation constant is introduced to improve the particle swarm optimization algorithm, and the controller parameters are optimized using the improved particle swarm algorithm to enhance the model’s stability and achieve automatic speed regulation of the motor.ResultsIn the dynamic experiment, it was proposed that the current fluctuation of the model was always kept within ±0.10A, demonstrating high stability. In addition, the research proposes that the maximum speed estimation error of the model is 5.77%, which is superior to the error calculation results of the comparison models and far less than the standard requirement of 8.00%.DiscussionThe model proposed in this study exhibits superior speed regulation performance, achieving high stability, low vibration, and strong robustness in motor automatic speed regulation control. It can better meet the speed control requirements in the field of motors, thereby better ensuring the safe operation of the motor.]]></description>
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