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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>
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        <pubDate>2026-08-06T02:02:30.347+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1884600</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1884600</link>
        <title><![CDATA[A review of composite mold design for battery enclosures and upper covers in new energy vehicles]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Yingshuai Liu</author><author>Xintong Li</author><author>Xiaobo Huang</author><author>Yang Wang</author><author>Jianwei Tan</author>
        <description><![CDATA[The rapid electrification of the automotive industry has created an urgent demand for lightweight, high-strength battery enclosure systems, positioning composite materials—including sheet molding compound (SMC), bulk molding compound (BMC), carbon fiber reinforced polymer (CFRP), and glass fiber reinforced polymer (GFRP)—as the dominant solution over traditional metallic alternatives. As the foundational tooling governing dimensional accuracy, surface quality, fiber orientation, and production efficiency, composite mold design is critical to translating the theoretical advantages of lightweight materials into manufacturing reality. This review provides a systematic synthesis of current knowledge on composite mold design for battery enclosures and upper covers, organized into five thematic sections covering mold structural design encompassing cavity architecture, mold material selection, and surface engineering; molding process control spanning compression molding, high-pressure resin transfer molding (HP-RTM), long fiber thermoplastic direct processing (LFT-D), and prepreg compression molding (PCM); simulation-driven design integrating finite element analysis (FEA), topology optimization, and ply-level analysis; intelligent mold technologies featuring real-time sensor monitoring, machine-learning-based process control, and predictive maintenance; and green manufacturing and multi-material integration addressing sustainability, recyclability, and regulatory compliance. The main contribution of this work lies in establishing a holistic framework that bridges material science, process engineering, and computational modeling for battery enclosure mold design, with particular emphasis on the convergence of additive manufacturing, artificial intelligence-driven optimization, and stringent safety regulations that will define the next decade of battery enclosure innovation. Compared to existing literature, this review uniquely integrates process-parameter mapping with mold-design requirements and provides actionable guidelines for selecting molding technologies based on production volume, weight reduction targets, and cost constraints.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1791839</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1791839</link>
        <title><![CDATA[Electromechanical virtual debugging and manufacturing system based on kinematic modeling and dynamic interference inspection]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ke Zhang</author><author>Liping Zhang</author><author>Xiujuan Meng</author>
        <description><![CDATA[IntroductionTraditional electromechanical manufacturing systems suffer from excessively long debugging cycles, elevated operating costs, and poor predictability of mechanical interference and collision hazards, which severely restricts on-site commissioning efficiency. To resolve these practical engineering bottlenecks, this study develops an integrated virtual debugging manufacturing system with dual core capabilities of high-precision kinematic calculation and real-time dynamic interference detection.MethodsFirst, a full-system kinematic model is established, and Lagrange’s equation is adopted to deduce multibody dynamic equations for precise prediction of motion states under complex working conditions. Second, a spatiotemporal coupling dynamic interference checking algorithm is invented to support instant quantitative evaluation of collision risks during equipment movement. Third, an intelligent material transfer path planning module is embedded, forming an all-in-one virtual debugging framework covering geometric modeling, interference inspection and automatic transfer control.ResultsBenchmark experiments reveal prominent performance gains of the proposed framework. The system shortens the physical debugging cycle by 62.7% and cuts overall debugging costs by 58.3%, with a fault resolution rate rising to 97.5%. The maximum promotion rate of debugging efficiency hits 239.1%, and motion repeatability accuracy is optimized to ±0.03 mm (a 75% improvement), alongside a 63.2% reduction in manual labor intensity. Real-time interference identification effectively eliminates unforeseen collision risks in physical commissioning.DiscussionThe measured indicators fully verify the reliable engineering performance of the proposed “modeling-interference-transfer” integrated architecture. This work delivers a novel low-cost, high-efficiency technical solution for the commissioning of electromechanical production lines, possessing broad practical application prospects in intelligent manufacturing workshops.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1842306</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1842306</link>
        <title><![CDATA[Enhanced speed sensorless DTC algorithm for asynchronous motor with long cables]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zitao Jin</author><author>Yonghong Deng</author>
        <description><![CDATA[IntroductionTo address the limitations of direct torque control in handling long cable configurations, this study proposes an enhanced direct torque control algorithm.MethodsThe proposed method employs Kirchhoff functions to calculate motor-side voltage and current, thereby improving control performance. To validate the proposed approach, Lyapunov stability analysis is conducted on the system function incorporating long cable and filter models. Furthermore, time parameters are introduced to verify system stability, confirming that the system remains stable in metastable states.ResultsSimulation and experimental results demonstrate that the torque improvement is most pronounced during rated-load startup. Experimental data confirm that the proposed method exhibits the most significant suppression of torque oscillations.DiscussionFuture work will focus on refining the algorithm to further address long cable power supply challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1863275</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1863275</link>
        <title><![CDATA[Comprehensive verification and correlation analysis of all factors of line loss data based on machine learning]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qing Xu</author><author>Weiwu Li</author><author>Zhengying Liu</author><author>Xinying Wang</author><author>Tianshou Li</author>
        <description><![CDATA[IntroductionA comprehensive examination is conducted on the distinctive loss patterns within a 10 kV distribution network, and the intelligent detection of abnormal line loss is realized by combining machine learning method.MethodsThe actual line loss data are first preprocessed. The K-means method is enhanced by integrating it with the Canopy algorithm for grouping line loss data. A genetic algorithm (GA)-optimized RBF neural network is then developed for line loss anomaly diagnosis. Finally, principal component regression analysis and the K-value method are applied to investigate the potential factors that are statistically associated with abnormal line loss.ResultsThrough a series of steps, a smart diagnostic system for distribution grid line loss anomalies is deployed, realizing the intelligent detection of abnormal line loss and the investigation of potential factors statistically associated with abnormal line loss.DiscussionThe proposed framework provides a scientific basis for formulating targeted loss management strategies in 10 kV distribution networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1839544</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1839544</link>
        <title><![CDATA[Fault diagnosis and prediction of industrial hydraulic pumps based on KOA-CNN-BILSTM model]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ting Zhong</author><author>Weiyi Zhu</author>
        <description><![CDATA[ObjectiveTo overcome the difficulties in feature extraction, hyperparameter determination, and health state evolution prediction in industrial hydraulic pump (IHP) fault diagnosis, this study proposes a hybrid deep learning model that integrates automatic feature learning, temporal dependency modeling, and global hyperparameter optimization.MethodsThe KOA-CNN-BILSTM model combines a Convolutional Neural Network (CNN) for extracting local vibration features, a Bidirectional Long Short-Term Memory (BILSTM) network for capturing forward and backward temporal dependencies, and the Kepler Optimization Algorithm (KOA) for optimizing key hyperparameters (learning rate, kernel numbers, and hidden units). Vibration signals were collected from an axial piston pump test bench under three pressure conditions (10, 15, and 20 MPa) and four health states (normal, plunger wear, valve plate wear, and swash plate looseness), with data preprocessing including outlier removal, normalization, and sliding window segmentation.ResultsThe proposed model achieves 97.6% diagnostic accuracy and 97.2% precision, with RMSE and MAE of 0.138 and 0.095, respectively, outperforming DRSN, TCN, and CNN-Transformer across all metrics. It converges rapidly, reaching 92% accuracy by the 60th epoch. Cross-condition tests maintain accuracy above 92% under all load combinations, demonstrating strong generalization. In wear evolution prediction, early warning is achieved at a wear area of 5 mm2, with final plunger wear probability reaching 95.2%.DiscussionThe KOA-CNN-BILSTM model exhibits comprehensive advantages including fast convergence, high accuracy, robust cross‐condition adaptability, and effective early warning capability. This study provides valuable technical support for intelligent condition-based maintenance of industrial hydraulic systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1805479</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1805479</link>
        <title><![CDATA[Multi-sensor fusion control technology and its automation implementation methods for electromechanical servo systems with nonlinear friction]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaojin Lu</author>
        <description><![CDATA[IntroductionComplex mechanical systems are affected by multiphysics coupling, nonlinear friction, and time-varying disturbances, making high-precision automated operation difficult. Directly mapping heterogeneous sensing data to real-time control actions remains challenging.MethodsThis study developed a multi-sensor fusion and adaptive control framework (AFCF) by integrating a Dual-Stream Attention Mechanism (DS-AM) with an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) controller. DS-AM decouples high-frequency vibration and low-frequency current features, while prioritized experience replay and dynamic constraints improve learning efficiency and torque-execution safety.ResultsAt 5 dB noise, AFCF achieved a tracking RMSE of 0.035 mm. For a variable-curvature butterfly trajectory, it limited the maximum contour error to 4.8 μm and estimated surface roughness to 0.52 μm. Under intermittent impact, it reduced energy consumption by 9.62% and peak mechanical acceleration by 45.6%.DiscussionAFCF integrates heterogeneous perception and adaptive control in a closed loop, supporting robust and energy-aware servo control under complex conditions. Further validation with physical hardware and lower-complexity models is required for broader deployment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1838542</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1838542</link>
        <title><![CDATA[Intelligent fault diagnosis method for rolling bearings based on adaptive feature mode decomposition and TCN-BiGRU-Attention]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingli Li</author><author>Zhu Yuan</author>
        <description><![CDATA[IntroductionIn complex working environments and noisy conditions, the vibration signals of bearings are highly non-stationary, and the fault characteristics are easily masked by noise, making it difficult to effectively identify the faults. Moreover, existing methods are relatively sensitive to parameter and working condition changes and lack diagnostic stability.MethodsTo address this issue, a smart fault diagnosis method based on the Time Convolution Network - Bidirectional Gated Recurrent Unit - Attention Model (TCN-BiGRU-Attention) was proposed. This method uses the Newton-Raphson optimization algorithm to optimize the parameters of the feature mode decomposition, extracts the feature modes, and combines them with the TCN-BiGRU-Attention deep temporal sequence model to achieve multi-scale feature modes and key temporal discrimination.ResultsThe experiments were conducted based on two public datasets - Case Western Reserve University and XJTU-SY, with each group of experiments repeated at least 10 times under the same initial conditions. At the same time, ablation experiments and performance comparison experiments with other advanced methods were carried out. The results show that in the fault identification task, the accuracy of this research method reached 96.32%, which is higher than 89.15% of one-dimensional convolutional neural networks, 91.45% of bidirectional long short-term memory neural networks, and 92.84% of time convolutional networks.DiscussionIn conclusion, this method can achieve high-precision and stable fault diagnosis for rolling bearings, providing an effective intelligent diagnosis solution for engineering applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1835857</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1835857</link>
        <title><![CDATA[System-level lightweight design of lower-limb exoskeletons: challenges and co-design strategies]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Liancheng Zheng</author><author>Rizauddin Ramli</author><author>Shengkui Yuan</author><author>Mohamad Hazwan Mohd Ghazali</author>
        <description><![CDATA[Wearable lower-limb exoskeletons have emerged as a promising solution for rehabilitation, mobility assistance, and human performance augmentation. However, their practical deployment is limited by lightweight design challenges, particularly the trade-off between structural stiffness and system mass, the increased inertial burden caused by distal mass distribution, and the difficulty of preserving torque transmission under wearable constraints. This mini-review summarizes recent advances in lightweight design from five perspectives: structural architecture, quantitative system mass, dynamic and human-exoskeleton coupled modeling, material selection, and actuation or transmission systems. Approaches such as topology optimization, hybrid architectures, high strength-to-weight materials, remote actuation, Bowden cable transmission, and high torque-density actuation are discussed in relation to these challenges. Overall, lightweight design is identified as a system-level co-design problem requiring coordinated optimization across mechanical structure, actuation, control, and user biomechanics. Future developments are expected to focus on integrated modeling, mechanism-level synthesis, and data-driven methods to improve performance and user adaptability in next-generation exoskeleton systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1853481</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1853481</link>
        <title><![CDATA[LHSNet: large kernel hierarchical shrinkage network with deep adaptive denoising and modern wide-conv architecture for robust fault diagnosis]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yue Zhao</author><author>Yingli Li</author><author>Fangwei Luo</author><author>Xi Chen</author><author>Hongqiao Yan</author><author>Molin Su</author><author>Jinhai Wang</author><author>Dechen Yao</author><author>Jianwei Yang</author>
        <description><![CDATA[Traditional intelligent fault diagnosis techniques often struggle with signal distortion in environments characterized by strong multi-source noise, where fault signals are easily submerged and feature extraction becomes extremely unreliable. Furthermore, intelligent diagnosis is difficult to achieve using traditional denoising techniques that require extensive prior expert experience. This paper proposes a novel one-dimensional modern convolutional neural network architecture with soft thresholding denoising modules, tailored for robust mechanical fault detection under complex noise interference. Firstly, adaptive noise reduction modules are developed to assign dynamic filtering parameters to individual vibration signals, which effectively eliminates multi-source noise interference and prevents fault signal distortion. Secondly, an adaptive feature normalization mechanism is integrated to enhance signal discrimination capability while maintaining computational efficiency. Lastly, comparative experiments and ablation studies are conducted under an extended signal-to-noise ratio and diverse noise type backgrounds including Gaussian, pink, and salt-and-pepper noise to verify the superiority of the proposed architecture over existing diagnostic methods. Results have well demonstrated that the proposed approach is able to reliably identify different fault types with over 98.00% median accuracy and 96.70% peak accuracy on standard benchmark datasets. Moreover, the proposed technique maintains high diagnostic performance across diverse operating conditions with less dependency on prior denoising expertise, which shows it is well suitable for practical industrial applications with complex noise interference.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1827441</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1827441</link>
        <title><![CDATA[Adaptive gait planning and performance evaluation of a lightweight three-bar tensegrity robot]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liancheng Zheng</author><author>Rizauddin Ramli</author><author>Ani Luo</author><author>Heming Zhao</author><author>Heping Liu</author><author>Guosong Chen</author>
        <description><![CDATA[This study presents a dynamic modeling and gait optimization framework for a mobile 3-bar tensegrity robot aimed at enhancing locomotion efficiency and stability on complex terrains. A complete Lagrangian dynamic model incorporating ground contact dynamics is established to accurately describe the nonlinear behavior of the tensegrity structure. Based on this model, a periodic actuation strategy is developed, and gait parameters for both straight-line and turning motions are optimized using the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective algorithm. The optimization simultaneously maximizes displacement and minimizes lateral deviation or centroid offset. A quantitative performance evaluation system which comprise of locomotion rate, specific energy consumption, and center-of-mass fluctuation is proposed to assess gait effectiveness. Simulation results demonstrate that increased active cable velocity improves motion efficiency and energy economy without compromising stability. The proposed method achieves a maximum straight displacement of 52 mm, a clockwise turning angle of 23°, and a counterclockwise turning angle of 13°. These results confirm that the proposed NSGA-II–based optimization framework effectively enhances the efficiency, stability, and adaptability of gait planning and control for lightweight tensegrity robots in unstructured environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1786455</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1786455</link>
        <title><![CDATA[Methods for compensating for positioning errors of rotary axes and improving machining accuracy in five-axis computer numerical control machine tools]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaorong Zhou</author><author>Lidong Huang</author><author>Xiaoping Liu</author>
        <description><![CDATA[IntroductionFive-axis CNC machine tools are essential for high-precision machining of complex curved surfaces, but the positioning errors of their rotary axes are geometrically amplified, severely affecting machining accuracy. Existing studies often focus on isolated technical aspects, lacking systematic and long-term effective solutions.MethodsThis paper proposes a systematic compensation scheme named SC-GEM, which integrates static geometric error modeling, S-curve acceleration/deceleration for dynamic optimization, and hardware upgrades including dual-lead preloaded worm gear pairs and high-resolution absolute encoders. A three-stage “measurement-calibration-consolidation” process is established to implement the compensation and ensure engineering repeatability.ResultsExperimental results show that key geometric errors of the C and A axes are reduced by 40%–60%, and dynamic following errors are suppressed by 60%–80%. Machining tests on an S-curve specimen demonstrate a 60.4% improvement in profile accuracy, a 66.7% reduction in error standard deviation, and an increase in tolerance-conforming points from 68.5% to 96.2%.DiscussionThe SC-GEM method achieves hardware-software synergy and static-dynamic integration, outperforming individual compensation strategies. The synergistic effect (1+1+1>3) validates the necessity of the integrated approach. Future work will focus on adaptive updating and real-time multi-source error compensation to enhance industrial applicability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1850568</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1850568</link>
        <title><![CDATA[A review on condition monitoring and control of industrial drilling systems: fault detection, fuzzy control, and human-induced faults]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Satyam Paul</author><author>Magnus Löfstrand</author>
        <description><![CDATA[This review presents a structured synthesis of recent advances in condition monitoring, fault diagnosis, and control of industrial drilling systems, with emphasis on rotary and rotary-percussive drilling. The paper is guided by a transparent review methodology and positions itself against recent review literature to clarify its scope and contribution. The reviewed studies are organized around four main themes: (i) fault classification across machine, rock-induced, human-induced, and environmental sources; (ii) condition-monitoring and fault-diagnosis methods based on multi-sensor measurements, signal processing, observer-based estimation, and data-driven intelligence; (iii) mathematical modelling of drill-string dynamics, stress-wave propagation, drill–rock interaction, and energy-transfer behaviour; and (iv) comparative evaluation of conventional, observer-based, machine-learning, fuzzy, and clustering-based control frameworks. Particular attention is given to recent developments in multivariate monitoring, wavelet-based anomaly localization, neural-network-assisted diagnosis, digital twins, and adaptive fuzzy control. A key conclusion of the review is that drilling anomalies cannot be interpreted reliably through a machine-only perspective; instead, they should be understood through a coupled machine-rock-human-environment framework. The paper further argues that future intelligent drilling systems should integrate signal fusion, mathematical modelling, hidden-state estimation, and human-aware supervisory logic, with hybrid architectures combining physics-based models, multivariate monitoring, machine learning, fuzzy reasoning, and digital twins representing the most promising pathway toward robust, interpretable, and industrially deployable drilling intelligence.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1826268</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1826268</link>
        <title><![CDATA[Defect detection and multi-scale feature fusion of cold rolled strip based on lightweight YOLOv11 algorithm]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yikao Liu</author><author>Tingting Xing</author><author>Jiuli Shen</author><author>Zhaoqing Chen</author>
        <description><![CDATA[IntroductionWith the rapid and intelligent development of the steel manufacturing process, efficient and reliable detection of surface defects in cold-rolled strip steel is of great significance to product quality and production safety. However, existing detection methods have limited ability to identify small defects, and some high-precision models have problems such as large parameter scale and insufficient real-time performance.MethodsThis study proposes a cold-rolled strip defect detection and MSFF method based on lightweight YOLOv11. This method introduces an MSFF structure at the neck of the detection network and combines it with a SimAM-Enhanced Block to enhance the expression of key defect features.ResultsExperiments demonstrate that the designed method achieves an mAP of 57.85% in the cold-rolled strip defect detection task (IoU threshold 0.50-0.95), which is 4.67% and 11.61% higher than YOLOv8n’s 53.18% and YOLOv5s’ 46.24%. It significantly reduces the risk of missed detection while maintaining high detection accuracy, and the recall rate reaches 88.12%, which is significantly higher than YOLOv8n’s 83.24%, indicating that it has stronger detection capabilities for small and low-contrast defects.DiscussionIn summary, the proposed method achieves a good balance between detection accuracy, robustness, and engineering deployability, and can provide an efficient and practical solution for online defect detection of cold-rolled strip steel.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1804611</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1804611</link>
        <title><![CDATA[Adaptive variable stiffness force position of grinding and measurement integration system combining human-computer interaction grinding system and multi-objective optimization algorithm]]></title>
        <pubdate>2026-05-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhipeng Yu</author><author>Zhijun Yang</author>
        <description><![CDATA[IntroductionRobotic grinding suffers from problems such as unstable contact force, limited control in large-area grinding, and difficulty in improving the grinding quality of complex curved surfaces.MethodsTo address these issues and achieve precise force-position control, adaptive adjustment, and multi-objective balance during the grinding process, this study designs a rigid-flexible coupling grinding mechanism and a human-machine interactive architecture for an integrated grinding and measurement system. A force-position trajectory optimization strategy grounded in an improved non-dominated sorting genetic algorithm II is proposed, and an adaptive strain stiffness force-position control algorithm based on impedance control is designed for the integrated grinding and measurement system.ResultsResults show that the improved non-dominated sorting genetic algorithm II achieves an accuracy of approximately 0.95 after 100 iterations, with the loss function stabilizing below the order of 10-4 after 40 iterations, and the delay time stabilizing at approximately 5 ms. The integrated grinding and measurement adaptive control algorithm maintains a surface roughness below 1.2 μm under different working conditions, achieving a material removal rate of 0.62 mm3/s in working condition 2, with a single-cycle total energy consumption as low as 1.78 kW·h.DiscussionThe integrated grinding and measurement adaptive variable stiffness force-position scheme, which combines a human-machine interactive grinding system with a multi-objective optimization algorithm, is effective and significantly improves overall grinding performance. This research provides a more efficient and precise control solution for the field of robotic grinding, which can be applied to the grinding of materials such as stainless steel, meeting the industrial demand for high-precision and low-energy grinding, and promoting the development of grinding automation technology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1809997</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1809997</link>
        <title><![CDATA[Optimized design of a multimodal perception system for sports robots based on YOLOv5 and KCF]]></title>
        <pubdate>2026-05-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ke Cai</author>
        <description><![CDATA[IntroductionFor motion robots that use dynamic perception, state-of-the-art systems still struggle to simultaneously tackle various challenges, including high-speed motion blur, strong interactive occlusion, and drastic changes in scene lighting, which limit the robustness and real-time performance of tracking.MethodsThis study proposes a multimodal perception optimization model that integrates multiple algorithms. The model first uses YOLOv5 to achieve rapid detection and localization of multiple object categories. Then, a nuclear correlation filter was integrated to achieve robust tracking of consecutive frames. Furthermore, spatiotemporal graph convolutional networks and dual channel attention mechanisms were introduced to enhance the extraction of motion patterns and key features. In addition, it also combines convolutional neural networks based on faster regions and deep simple online real-time tracking to support high-precision detection and identity preservation. Finally, apply genetic algorithm to dynamic parameter optimization and adaptive adjustment.ResultsThe proposed model achieved a maximum multi-target tracking accuracy of 96.23% on the SportsMOT dataset and AthletePose3D dataset, with a tracking accuracy improvement of 50.44%, consistently outperforming the baseline method, and a minimum end-to-end perception delay of 49.6 ms.DiscussionThe model demonstrates excellent tracking stability and real-time response capability in various dynamic scenarios, achieving high accuracy and robustness in target perception and trajectory prediction, and is expected to support intelligent sports applications, including training assistance, real-time opponent simulation, and autonomous interception decision-making.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1679715</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1679715</link>
        <title><![CDATA[Online monitoring method for operation status of the thermal power generating units based on fusion of limit learning machine and SCADA data]]></title>
        <pubdate>2026-05-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yongjun Fu</author><author>Xuyang Zhao</author><author>Zhiqiang Yang</author><author>Yujiang Zhang</author><author>Yaping Qv</author><author>Baohua He</author><author>Yuewu Yang</author>
        <description><![CDATA[To quickly learn the complex operational patterns of thermal power generating units, this paper proposes an online monitoring method based on extreme learning machine (ELM) and SCADA data. First, health data from normal operations of the thermal power generating set are collected, and outliers unrelated to the unit’s operating conditions are removed from the SCADA data, along with local abnormal points. Using this processed data, an extreme learning machine model is trained and constructed. The initial hidden layer output matrix and weights are obtained by adjusting the hidden layer output weights and training the model with the SCADA health dataset. During the updating phase, the model continuously learns from the input SCADA health data and updates the hidden layer output matrix and weights in real time until training is completed, enabling online monitoring of the thermal power generating set’s operational state. The experimental results demonstrate that the proposed method can track the operating state of thermal power generating units in real time and detect abnormalities or faults. When the number of hidden layer nodes reaches 75, the gradient vanishing curve of the Tanh function initially lies below that of the RBF function but gradually rises and surpasses it. On the test set of SCADA dataset 5 for thermal power generating units, as the number of hidden layer nodes increases, the gradient vanishing curves of all activation functions exhibit an upward trend. The adopted extreme learning machine model demonstrates the best performance in predicting and classifying the unit’s operating state, improving accuracy and showing higher efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1824638</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1824638</link>
        <title><![CDATA[An improved limited-compensation flux observer for modular multilevel high-voltage inverters and its application in rail vehicle traction system optimization]]></title>
        <pubdate>2026-05-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuxiao Li</author><author>Changxiu Yang</author>
        <description><![CDATA[Modular multilevel high-voltage inverters are core components of rail vehicle traction systems, and their control performance directly determines the energy efficiency and operational reliability of traction drives. The primary objective of this study is to overcome the inherent limitations of traditional voltage model flux observers—specifically waveform distortion and DC component interference—which severely degrade the precision of rotor flux-oriented vector control (FOC) systems. To address this problem, an improved voltage model flux observer with limited compensation is proposed in this paper. The observer introduces Cartesian-polar coordinate transformation and flux linkage amplitude limiting with constant angle in the feedback loop, which effectively eliminates waveform distortion and significantly reduces DC component interference in flux observation. Based on the nonlinear mathematical model of three-phase asynchronous motors, we established both closed-loop and sensorless vector control systems for modular multilevel high-voltage inverters. Systematic simulation verifications were conducted under working conditions of no-load startup, abrupt load change, and motor forward/reverse rotation. Our findings quantitatively demonstrate the superiority of the proposed method: the voltage and current harmonic contents are drastically suppressed, achieving a highly optimized total current distortion rate (THD) of exactly 1.3%. Furthermore, the sensorless control system achieves exceptional speed tracking with a maximum transient error of less than 40 r/min during reversal and negligible steady-state fluctuations. The proposed improved flux observer is well-suited for rotor flux-oriented vector control systems, providing robust theoretical and simulation support for enhancing the energy efficiency of rail vehicle traction systems in high-voltage engineering scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1823141</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1823141</link>
        <title><![CDATA[Time-optimal trajectory planning for robotic manipulators based on an improved pufferfish optimization algorithm]]></title>
        <pubdate>2026-05-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaofang Guo</author><author>Kaiwei Zhuang</author><author>Yifang Gao</author><author>Yongqiang Xiao</author><author>Xinmeng Wu</author>
        <description><![CDATA[Time-optimal trajectory planning is an important topic in robotic manipulation and automated system operation, particularly when joint motion must satisfy predefined velocity and acceleration constraints. This paper presents a trajectory planning approach that combines a 4-3-4 hybrid polynomial interpolation method with an improved pufferfish optimization algorithm (IPOA). The main contributions are two-fold: (1) a chaotic mapping initialization strategy based on the Logistic map to enhance population diversity, and (2) an adaptive parameter mechanism that adjusts particle movement using the mean position of superior individuals. Unlike existing metaheuristic trajectory planners that directly apply standard PSO, WOA, or GA variants, the proposed IPOA introduces these two mechanisms specifically tailored for the high-dimensional, constrained 4-3-4 polynomial time-optimization problem, providing both theoretical motivation and empirical superiority over POA and other state-of-the-art algorithms. Benchmark function experiments indicate that, under the evaluated settings, IPOA achieves faster convergence and better stability than several commonly used metaheuristic algorithms such as POA, PSO, ZOA, GWO, and WOA. When applied to a six-degree-of-freedom robotic manipulator, the method generates joint angle, velocity, and acceleration trajectories that remain smooth and continuous, without abrupt variations, and consistent with the imposed motion limits. Experiments carried out on an actual robotic platform further show that the planned trajectories can be executed reliably and reproduce the expected motion behavior. These results suggest that IPOA provides a feasible and effective method for addressing constrained, time-related trajectory optimization problems in robotic and automated applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1797900</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1797900</link>
        <title><![CDATA[Research on adaptive optimization of unmanned aerial vehicle transmission line inspection imaging under strong and weak light conditions]]></title>
        <pubdate>2026-04-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haibo Ru</author><author>Ziqiang Lu</author><author>Jie Li</author><author>Huiwei Liu</author><author>Ziying Lu</author>
        <description><![CDATA[This study focuses on the problem of image quality degradation caused by drastic changes in light intensity during unmanned aerial vehicle inspection of power transmission lines, and proposes a three-level adaptive optimization framework of light perception, parameter adjustment, and image enhancement. This framework precisely identifies the light conditions through a multi-feature fusion model (with an accuracy rate of 97.3% for light scene classification), dynamically optimizes the core parameters of the camera based on reinforcement learning, and completes image enhancement by combining the lightweight network LW-EnhanceNet. Experiments show that the proportion of overexposure in strong light scenarios has decreased from 35.2% to 7.8%, the signal-to-noise ratio in weak light scenarios has reached 37.5 dB (an increase of 54.9% compared to the original image), and the F1-score for defect detection has reached 89.7%; in real scenarios, the rate of image quality meeting standards has increased to 91.0%, and the inspection efficiency has increased by 24.8%. This method provides an effective solution to the imaging bottleneck in all-weather inspections, and has significant theoretical and practical significance for improving the automation level of inspections and the accuracy of defect detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1781086</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1781086</link>
        <title><![CDATA[Intelligent fault diagnosis of coal mine roadheaders based on particle swarm optimization of BP neural networks]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Junling Feng</author><author>Ye Zhang</author><author>Ailiang Kang</author>
        <description><![CDATA[IntroductionCoal mine roadheaders operate under complex conditions characterized by prolonged exposure to high vibration and dust levels, resulting in a high failure rate. Traditional fault diagnosis methods suffer from issues such as low diagnostic accuracy and poor real-time performance. This study suggests an intelligent diagnosis model for coal mine roadheader faults based on the artificial fish swarm algorithm, particle swarm optimization, and a backpropagation neural network in an attempt to improve the precision and effectiveness of fault diagnosis for coal mine roadheaders and guarantee safe equipment operation.MethodsInitial data is processed through statistical methods, correlation analysis, and normalization. A backpropagation neural network is selected as the fundamental diagnostic model, with the artificial fish swarm algorithm and particle swarm optimization algorithm introduced to perform global optimization of its initial weights and thresholds. The network is trained using forward propagation and error backpropagation mechanisms, and its outputs are converted into fault probability distributions.ResultsExperimental outcomes indicated that the training loss and test loss values of the research method differed by 0.01. Its classification accuracy remained consistently above 95% across varying TS proportions. In practical application testing, the best fitness and AF of the research method both exceeded 0.8 overall. Its omission rate reached a stable value of 9.8% at an 80% load rate.DiscussionThe above results demonstrate that the research methodology exhibits high diagnostic accuracy and efficiency, effectively addressing issues such as insufficient precision and low detection efficiency in traditional approaches. This enhances the safety and reliability of intelligent fault diagnosis for coal mine roadheaders.]]></description>
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