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        <title>Frontiers in Mechanical Engineering | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/mechanical-engineering</link>
        <description>RSS Feed for Frontiers in Mechanical Engineering | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-14T13:43:31.804+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1869330</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1869330</link>
        <title><![CDATA[A flexible deep learning-based surrogate-assisted genetic algorithm framework for engineering design optimization, with application to industrial grating structures]]></title>
        <pubdate>2026-08-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tran Van Thien</author><author>Nguyen Minh Tien</author><author>Bui Tuan Anh</author><author>Ngoc Tam Bui</author><author>Hiroshi hasegawa</author>
        <description><![CDATA[While Artificial Intelligence (AI) and Machine Learning (ML) hold significant promise for Engineering Design Optimization (EDO), traditional optimization approaches frequently suffer from excessive computational and time expenses. To overcome these barriers, this study introduces a high-performance optimization framework built upon three core contributions. First, a cost-effective data acquisition strategy is proposed, utilizing existing manufacturer catalogs alongside data augmentation methods to produce high-quality datasets with minimal resource expenditure. Second, an automated, Genetic Algorithm (GA)-driven approach is designed to optimize the hyperparameters of a Deep Neural Network (DNN), successfully eliminating the reliance on manual expert calibration. Third, a Surrogate-Assisted Genetic Algorithm (SAGA) is deployed, leveraging the highly accurate DNN as a surrogate model to rapidly navigate discrete design spaces and circumvent computationally exhaustive simulations. The practical viability of the framework was rigorously evaluated through an industrial steel grating design application. Empirical outcomes indicate substantial real-world utility, yielding an average mass reduction of 23.21±0.65% across 44 standardized configurations without violating structural or serviceability constraints. The optimization pipeline exhibited remarkable computational efficiency, completing the task in just 3.2 min per model. This acceleration is directly facilitated by the high-fidelity surrogate model, which delivered a classification accuracy of 97.678±0.472% and robust mass prediction metrics (R2=0.999, MAE = 0.719±0.101 kg/m2). Additionally, the methodology demonstrated excellent adaptability by successfully resolving the classical 200-bar truss benchmark subject to strict displacement and frequency constraints. Demonstrating superior performance over existing baseline approaches in both solution quality and execution time, this study substantiates the framework as a highly flexible, scalable, and robust approach for resolving complex engineering design challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1885902</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1885902</link>
        <title><![CDATA[Pressure contour engineering for highly efficient ground effect flight]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adam B. Suppes</author><author>Galen J. Suppes</author>
        <description><![CDATA[IntroductionAn ideal flight mechanism model reasonably approximates the efficiency of aerodynamic flight, similar to how ideal heat engines approximate what is possible with different engine designs. It is useful for modeling ground effect flight. The ideal model provides a benchmark against which vehicle prototype performances may be compared to rapidly assess design effectiveness; similar benchmarks have been absent to date.MethodsThe ideal flight mechanism model equation was derived from force and energy balances on aircraft in flight to preserve reversible losses. Data for the paper was calculated through computational fluid dynamics (CFD) to reasonable estimate aircraft performance.ResultsCFD performances of better performing airfoils and digital prototypes approach the lift-to-drag ratio (L/D) of the ideal mechanism model. The prominent operational parameter of the ideal equation model in ground effect flight is the ratio of the vertical perimeter area below the vehicle to the planform area. The variable is applicable for unifying two- and three-dimensional comparisons. Digital prototypes with aspect ratios less than 0.4 have lower L/D estimates than model projections, a finding identified for further study to better understand how to improve performance at low aspect ratios.DiscussionDigital prototype performances were evaluated in three phases of flight: (a) takeoff, (b) cruising velocities, and (c) higher speed travel. For takeoff, hovercraft functionality may be used, but analysis indicates that wheeled suspension is more efficient. Cruising velocities operate most efficiently with ram effect lift generated in the lower cavity of the lower ground effect flight transit (GEFT) vehicle, which is capable of approaching model predictions. Vertical ducts with fans passing through the fuselage are analyzed to extend cruising travel conditions over a greater velocity range to maintain cavity pressures and sufficient lift. At higher speeds, lower lift coefficients are needed to maintain ground effect flight. Trailing-edge stagnation plates (i.e., spoilers) may be used to reduce drag under these conditions. Although jet aircraft travel at higher altitudes to reduce drag, ground effect vehicles may use stagnation plates to achieve higher flight efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1897119</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1897119</link>
        <title><![CDATA[Analysis of multiphase coupling in hydraulic collection heads considering sediment effects for deep-sea mining]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bo Shen</author><author>Fangjie Zhai</author><author>Xiaoxiang Wang</author>
        <description><![CDATA[In deep-sea mining operations, the presence of sediment has a significant impact on hydraulic extraction performance. However, existing studies generally assume a rigid substrate, failing to reflect the effects of real sedimentary environments. To evaluate the adaptability of different hydraulic extraction technologies under sediment-laden boundary conditions, this study coupled VOF interface tracking, discrete element particle dynamics, and a non-Newtonian rheological model to establish a jet-particle-sediment multiphase numerical method. It systematically compared the extraction performance of three mainstream extraction heads: suction-lift, wall-following jet, and jet-flushing. The results indicate that sediment significantly inhibits extraction efficiency. Among the methods, the suction-lift method is most severely affected. The wall-following jet method causes the least disturbance to the environment. However, its extraction capacity is limited. The jet-flushing method is least affected by sediment. It also exhibits the best overall performance. Furthermore, particle dynamics analysis reveals two typical modes of collection failure: escape and retention. Escape failure stems from a lack of lift, causing particles to escape the collection zone without being effectively retained; retention failure results from a spatial lag in the onset of lift, where particles acquire lift but have already missed the optimal lifting position. This study clarifies the significance of sediment in the analysis of hydraulic mineral collection for deep-sea mining and reveals the mechanical mechanisms underlying particle collection failure, thereby providing a theoretical basis for the selection and optimized design of deep-sea mining equipment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1888696</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1888696</link>
        <title><![CDATA[Physics-guided digital-twin-ready surrogate framework for predictive design of double-bridge compliant mechanisms in advanced manufacturing systems]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vijay Dilip Kolate</author><author>Pradipkumar Dudhajirao Darade</author><author>Suhas P. Deshmukh</author>
        <description><![CDATA[The integration of physics-based modeling and data-driven prediction is creating new opportunities for predictive design, optimization, and the deployment of digital twins in advanced manufacturing systems. In compliant mechanisms, particularly double-bridge configurations used in precision positioning and surface engineering applications, accurate prediction and optimization of amplification ratio remain challenging due to coupled geometric interactions and nonlinear design trade-offs. This study presents a Physics-Guided Digital-Twin-Ready Framework for the predictive design and multi-objective optimization of double-bridge compliant mechanisms. A physics-consistent dataset comprising 8,000 design samples was generated using Latin Hypercube Sampling, analytical compliance modeling, constraint-based filtering, and response-space stratified sampling. The resulting dataset provides balanced coverage of amplification ratios within the range of 5–50, enabling robust learning across diverse design regimes. Machine-learning models, including Random Forest and Extreme Gradient Boosting (XGBoost), were developed to predict amplification ratio from geometric and material parameters. The models achieved excellent predictive performance, with coefficients of determination (R2) exceeding 0.99, mean absolute errors below 0.93, and root mean square errors below 0.65. Uncertainty quantification was incorporated through ensemble variance estimation, yielding prediction intervals with less than 5% relative uncertainty in well-sampled regions. SHAP-based explainability and sensitivity analyses revealed that amplification behavior is primarily governed by geometric parameters, particularly beam lengths and flexure thickness, whereas material stiffness has comparatively lower influence. NSGA-II-based multi-objective optimization identified Pareto-optimal solutions that balance amplification ratio and equivalent stiffness, highlighting the inherent trade-off between displacement amplification and structural rigidity. The developed surrogate models enable rapid design exploration, uncertainty assessment, and optimization, while achieving computational speed-ups of approximately 103–107 times compared with finite-element-based evaluation workflows, depending on the evaluation method. The primary contribution of this work is the integration of analytical compliance modeling, physics-consistent dataset generation, uncertainty-aware machine learning, explainable artificial intelligence, and multi-objective optimization within a unified predictive framework. The proposed methodology should be interpreted as a digital-twin-ready surrogate architecture rather than a fully implemented digital twin, as real-time sensing, and online model updating are beyond the scope of the present study. Nevertheless, the framework provides a scalable foundation for future integration with experimental measurements, multi-fidelity datasets, and digital-twin-enabled manufacturing environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1861181</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1861181</link>
        <title><![CDATA[Sensing and assist-as-needed control in lower-limb rehabilitation exoskeletons]]></title>
        <pubdate>2026-08-10T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Zheng Liancheng</author><author>Rizuaddin Ramli</author><author>Li Mingtao</author><author>Liu Chunhui</author>
        <description><![CDATA[Lower-limb rehabilitation exoskeletons are often discussed in terms of mechanics, sensing, and control, yet their rehabilitation value depends on how these elements work together during human–robot interaction. This review focuses on the integration of sensing, compliant actuation, and assist-as-needed control in lower-limb rehabilitation exoskeletons. Recent research suggests that effective assistance depends not only on actuator output, but also on reliable gait-state detection, intention-related sensing, mechanical transparency, and real-time adaptation. Current progress in sensing based on inertial measurement units (IMUs), force and pressure measurements, and electromyography (EMG) is reviewed, followed by discussion of how actuation choice and mechanical compliance influence safe and effective assistance. Major control strategies, including trajectory tracking, impedance control, hierarchical control, learning-based methods, and assist-as-needed approaches, are then compared. Remaining barriers to clinical translation include signal instability, safety and certification requirements, and the persistent gap between laboratory performance and patient-specific rehabilitation needs. Future progress will likely depend on tighter co-design of sensing, hardware compliance, and cooperative control.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1888723</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1888723</link>
        <title><![CDATA[Deep learning-based time-series solar power prediction for automatic multisource energy generation systems]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nathaniel Abidemi Akinrinade</author><author>Ayodele Samuel Onawumi</author><author>Emmanuel Olayinka Sangotayo</author><author>Oluwagbemileke Olofinbiyi</author><author>David Temiloluwa Babalola</author><author>Lanre Daniyan</author><author>Humbulani Simon Phuluwa</author><author>Kehinde Alaga</author><author>Ilesanmi Daniyan</author>
        <description><![CDATA[The increasing need for energy security and the development of clean energy sources to mitigate the impact of climate change necessitate the development of multisource energy generation systems comprising solar panels, generators, and grids. Four hybrid deep learning models were developed to predict the solar power generation (SPG) from historical solar panel data. These include a time convolutional network with enhanced multilayer perceptron (TCN-EMLP), a convolutional neural network with long short-term memory (CNN-LSTM), an LSTM with AutoEncoder (LSTM-AE), and a transformer model. These models were deployed for the predictions of solar power generation (SPG). All models were applied to a dataset containing 378 observations of solar energy data, allowing for a direct comparison between the hybrid deep learning methods employed. The input variables used include battery level (BL), ambient temperature (Temp), solar Irradiance (Ir). The results indicated that LSTM-AE showed superior performance relative to TCN-EMLP, LSTM-AE, and the transformer model with a strong R2 of 0.8359, a summary R2 of 0.8059, a root mean square error (RMSE) of 7.4304 W, and a mean absolute error (MAE) of 5.9322 W. CNN-LSTM achieved a significantly high performance comparable to TCN-EMLP and the transformer model. The utilization of deep learning to build intelligent automated multisource energy systems could lead to enhanced prediction accuracy, better performance, and higher sustainability by lessening reliance on non-renewable backup systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1896770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1896770</link>
        <title><![CDATA[An empirical assessment of assembly line productivity constraints in automotive manufacturing systems using statistical analysis]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Avinash Gosavi</author><author>Padmakar Shahare</author><author>Swapnil Gund</author><author>Prashant Paraye</author><author>Twinkle Choudhary</author>
        <description><![CDATA[IntroductionAssembly line productivity is a critical performance factor in passenger car manufacturing because it directly affects output stability, production cost and delivery efficiency. Existing studies discuss machine, manpower, supply chain and quality issues separately, but limited empirical work examines these barriers together in passenger car manufacturing units of the Pune region. To address this gap, the present study provides an integrated empirical assessment of productivity constraints by combining barrier ranking, interrelationship analysis and factor-based classification within a single quantitative framework. This study aims to identify, evaluate and classify the major barriers affecting assembly line productivity using quantitative industry responses.MethodsA structured questionnaire was used to collect responses from 535 respondents associated with passenger car manufacturing operations. The data were analyzed using frequency analysis, descriptive statistics, correlation analysis and exploratory factor analysis.ResultsThe results showed that lack of modern tools or outdated machinery was the most critical barrier with 97.0% total agreement and the highest mean score of 4.60. Inadequate maintenance followed with 95.5% agreement and a mean score of 4.54. Frequent product defects recorded 93.7% agreement and a mean score of 4.42. Equipment breakdowns recorded 89.3% agreement while customer complaints due to defects recorded 85.3% agreement. Correlation analysis showed strong association between absenteeism or operator delays and poor worker coordination with r = 0.685. Factor analysis extracted four major productivity dimensions with a KMO value of 0.863 and 58.223% total variance explained.Discussion and ConclusionThe main contribution of the study is the identification of statistical associations and shared dimensions among technical, workforce, supply-logistics and quality-related barriers. The study concludes that productivity improvement needs integrated action through technology upgradation, preventive maintenance, defect control, logistics improvement and workforce coordination. Beyond the Pune automotive cluster, the findings provide useful guidance for passenger-car assembly units and similar manufacturing systems in emerging industrial regions where perceived productivity constraints are associated with machine, workforce, material-flow and quality-control limitations. The study is limited to questionnaire-based responses from selected units and therefore the results should be generalized cautiously without plant-level longitudinal validation. Future work may apply regression, SEM or machine-learning models for predictive validation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1904143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1904143</link>
        <title><![CDATA[Predictive current control strategy for PMSM based on NADRC]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenjing Chen</author>
        <description><![CDATA[IntroductionPredictive current control (PCC) for permanent magnet synchronous motors (PMSM) exhibits slow response and obvious chattering under parameter variation and load shock, while existing schemes cannot coordinate anti-disturbance performance, dynamic speed and battery power constraints.MethodsThis paper designs an improved dynamic double-power reaching law (DPRL) with finite-time convergence and low chattering, embeds it into nonlinear active disturbance control (NADRC) coupled with an extended sliding mode disturbance observer, and adds a battery power limiting module. Simulations and dual-motor bench tests are implemented with multiple contrast algorithms and ablation groups.ResultsThe proposed strategy achieves zero overshoot across all test conditions. During sudden 10 N·m load, the speed drop is only 285 r/min with 1.5 s recovery; acceleration and reversal response time are reduced by 40% and 70% respectively, and d/q‐axis current ripples are significantly weakened. DiscussionThe integrated DPRL‐NADRC PCC enhances PMSM robustness and dynamic performance under complex disturbances and power constraints. Future work will develop automatic gain tuning algorithms and validate the method under high‐speed demagnetization and multi-motor operating scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1877194</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1877194</link>
        <title><![CDATA[Artificial intelligence for prognostics and health management in off-highway vehicles: a systematic review of methods, data challenges, and deployment considerations]]></title>
        <pubdate>2026-08-06T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Yuvraj Patil</author><author>Virendra Bhojwani</author><author>Sachin Pawar</author><author>Pralhad Tipole</author><author>Goutam Mohapatra</author>
        <description><![CDATA[Off-highway machines, agricultural harvesters, construction excavators, and mining haul trucks operate under extreme load variability, harsh unstructured environments, and constrained sensor instrumentation, creating prognostic conditions fundamentally different from on-road vehicles. While AI-enabled predictive maintenance has matured for passenger vehicles and well-instrumented industrial assets, and off-highway telematics adoption is expanding rapidly, its translation to these software-defined field machines remains insufficiently addressed. This systematic review synthesizes AI-driven prognostic methods, data challenges, and deployment considerations specific to off-highway operation. Following a PRISMA 2020 protocol, the 2014–2025 literature is screened across seven databases, with the 51 studies retained for synthesis additionally quantified by method family, equipment sector, and publication year to expose the relative scarcity of off-highway-specific evidence, and a wide range of methodologies is synthesized, from foundational supervised learning (SVMs, Random Forests) and advanced deep learning (CNNs, LSTMs for RUL prediction) to unsupervised (Autoencoders), ensemble, and transfer-learning techniques. The review contrasts the primary prognostic frameworks—data-driven, physics-based, and hybrid—and the role of knowledge-based expert systems in delivering interpretable alerts. A significant focus is placed on the data pipeline, including sensor selection strategies, data quality, feature engineering, severe class imbalance, and labeling complexity. Implementation hurdles such as operating-condition variability, model validation, the computational constraints of edge devices, and Explainable AI (XAI) are further examined, with a critical analysis of where each method degrades under field variability. Finally, emerging directions are explored, including Digital Twins and Edge Computing, closing with reformulated, off-highway-specific research gaps for real-world deployment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1914977</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1914977</link>
        <title><![CDATA[Road-network accessibility and POI-based spatial prioritization of EV charging infrastructure in Ningbo, China]]></title>
        <pubdate>2026-08-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weixin Zhu</author><author>Jie Wei</author><author>Huanqin Lv</author>
        <description><![CDATA[ObjectiveThis study evaluates POI-based spatial accessibility to candidate electric-vehicle charging facilities in central Ningbo and develops a transparent screening framework for identifying urban destinations with comparatively weak road-network access.MethodsCandidate charging-facility POIs and urban functional POIs were collected through the Amap Web Service API from 20 to 30 June 2026. A directed OpenStreetMap motor-vehicle network was used to calculate nearest-facility road-network distance. Network and Euclidean distances were compared directly; fixed-rate energy use was reported only as an illustrative conversion of access distance. Non-parametric group comparisons, alternative priority-weighting schemes, and outlier-exclusion tests were used to assess robustness.ResultsThe final inventory contained 1,306 candidate charging-facility POIs and 9,426 urban functional POIs; 32 functional POIs were unreachable on the constructed network. The median network distance was 0.626 km, compared with a median Euclidean distance of 0.365 km, and the median detour ratio was 1.577. Network distance exceeded Euclidean distance for 93.59% of valid pairs (Wilcoxon p < 0.001). District and POI-category differences were significant, with Beilun and tourism/park POIs showing the greatest mean access distances. The baseline priority model classified 1,409 POIs as high priority and 940 as very high priority; alternative weighting schemes retained 76.5%–93.8% of the baseline top-quartile set.ConclusionThe proposed contribution is a reproducible, data-light diagnostic workflow that integrates network topology, destination context, direct Euclidean comparison, and uncertainty testing. The results identify areas for further operational investigation rather than verified demand shortfalls or construction sites. No user-behaviour, charging-transaction, station-capacity, queueing, or route-specific vehicle-energy data were available.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1903329</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1903329</link>
        <title><![CDATA[MXene-based soft interfaces for biomechanical engineering: from deformation sensing to bioelectronic and tissue-repair systems]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Haotian Wu</author><author>Wenyue Si</author><author>Peikai Zhang</author><author>Jiawen Tian</author><author>Bangsheng Yin</author>
        <description><![CDATA[MXenes have been widely reviewed as biomedical nanomaterials for sensing, therapy, imaging, drug delivery and tissue engineering. However, most reviews organize MXene studies by biomedical application categories rather than mechanical conditions under which MXene-containing interfaces operate. This leaves an important gap for biomechanical engineering: how MXene-based soft interfaces maintain signal transduction, transport and biological contact when bent, stretched, compressed, hydrated or attached to moving tissues. This mini review addresses that gap by evaluating MXenes as biomechanical interface materials rather than isolated conductive nanofillers or biomedical additives. The rapid growth of MXene-based soft sensors, bioelectronic devices and tissue-contacting systems highlights the need to assess whether these materials can meet translational requirements for stable, deformable, reproducible and biologically compatible interfaces. The review is structured around systems in which mechanical deformation, hydrated transport, electron–ion conduction and biological contact directly affect device function, with representative examples including wearable deformation sensors, electronic skins, hydrogel electrodes, deformable biosensors, wound-contact interfaces and regenerative scaffolds. Applications dominated by drug delivery, cancer therapy, bioimaging, implant coatings or static antibacterial activity are not comprehensively reviewed unless they clarify deformation-dependent transport, tissue contact or interface reliability. We critically compare how surface terminations, oxidation state, flake size, percolation networks, polymer bonding, swelling, modulus matching and biological boundary conditions regulate biomechanical functions. Key challenges include aqueous instability, storage-related degradation, calibration drift, motion artifacts, fatigue, sterilization tolerance and incomplete biological testing. We propose a four-layer interface framework and validation priorities that pair biomechanical testing with cytotoxicity, irritation, inflammatory response and reproducibility assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1893961</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1893961</link>
        <title><![CDATA[GAESA-iFormer: a graph attention-enhanced scale-aware inverted transformer for performance characterization of aircraft engine compressors]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiang Gao</author><author>Zhanxue Wang</author><author>Zhenyu Tao</author>
        <description><![CDATA[IntroductionAccurate determination of aircraft engine compressor characteristics is vital for stability and performance optimization. However, experimental data acquisition is often hindered by high costs, resulting in a strong reliance on limited test data.MethodsTo address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network (GAT) encoder, a Multi-scale Feature Enhancement (MSFE) module, and a cross-attention decoder based on the inverted Transformer architecture. The GAT encoder explicitly models relational dependencies among neighboring operating points along each speed line, capturing local continuity while reducing computational complexity. The MSFE module employs parallel convolutional kernels of sizes 1, 3, and 5 to extract multi-granularity features, effectively broadening the receptive field. Furthermore, the cross-attention decoder enables explicit knowledge transfer across different speed lines, leveraging structural similarities between well-sampled and sparsely sampled speeds—a critical advantage when data is scarce.ResultsComprehensive evaluations demonstrate that GAESA-iFormer achieves optimal performance with a feature dimension of 64 and four encoder layers. When trained on transformed secondary data, the proposed model significantly outperforms state-of-the-art baselines, reducing the RMSE by 27.03% and MAE by 31.78%.DiscussionThe results indicate that the proposed GAESA-iFormer model achieves improved prediction accuracy on the current compressor dataset under limited experimental data.]]></description>
      </item><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.2026.1892426</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1892426</link>
        <title><![CDATA[Mode switching control strategy for dual-drive transmission system of pure electric vehicles]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kai Wang</author>
        <description><![CDATA[IntroductionCurrent studies mostly realize local single-condition optimization without full-process bidirectional coordinated control, failing to balance smoothness, durability and response speed simultaneously.MethodsComplete bidirectional switching workflows for single-motor independent drive, torque coupling and speed coupling modes are designed with calibrated torque, speed and pressure thresholds; a multi-objective function containing impact intensity, sliding friction work and switching time is built with hierarchical hard/soft constraints, and MPC rolling optimization is adopted to solve the constrained problem; comparative simulations, anti-disturbance robustness tests and real-time performance evaluation are implemented on MATLAB/Simulink.ResultsThe peak impact intensity under all conditions is controlled within 8.0 m/s3 much lower than the industry limit. Compared with traditional SQP single-objective optimization, the average impact reduces by 38.8%, total sliding friction work cuts by 19.1%, switching response speeds up by 17.0%, and the mode switching success rate reaches 99.6%. Under ±30% parameter perturbations, the maximum impact change rate is merely 7.4%, and the algorithm single-step delay is 0.6 ms with only 28.6% hardware resource occupation, fully meeting vehicle real-time control demands.DiscussionThe presented strategy significantly suppresses switching shock and enhances system robustness, providing theoretical and technical support for dual-drive transmission control. Only simulation verification is completed at present; subsequent hardware-in-the-loop, powertrain bench and real vehicle road tests will be conducted to further verify its practical engineering performance.]]></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.2026.1765511</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1765511</link>
        <title><![CDATA[Effect of active thermal conductive medium microelectronic inkjet coating technology on the processing performance of workpieces]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haijuan Liu</author><author>Baolin Peng</author>
        <description><![CDATA[IntroductionIn view of the high cutting temperature, severe tool wear and poor processing surface integrity caused by poor thermal conductivity of the material during the cutting process of workpieces, active thermal conductive medium microelectronic inkjet coating technology is proposed.MethodsThis technology applies a uniform thermally conductive medium layer on the surface of a pretreated workpieces through a scraping process. Three surface active media are used: copper powder thermally conductive glue, thermally conductive silicone grease and liquid graphene. A cutting temperature prediction model that considers both mechanochemical effects and enhanced heat transfer effects is built.ResultsExperimental results showed that the medium-enhanced heat transfer effect increased as its thermal conductivity increased, but the improvement tended to be flat after exceeding 80W/(m· °C). Compared with the state without media, coating with high thermal conductivity graphene media significantly reduced the tangential cutting force by 11.51%, lowered the chip thickness by 31.48%, and reduced the chip temperature and workpiece surface temperature by 12.13% and 27.37%. The residual tensile stress decreased by 25.18%, the surface roughness decreased from 0.54 μm to 0.41 μm, the surface microhardness decreased by 7.64%, and the depth of the hardened layer decreased by 32.16%.DiscussionThis technology exerts an efficient heat dissipation function of the medium, which can significantly inhibit the accumulated cutting heat, improve plastic deformation and optimize surface integrity, providing a feasible process solution for efficient and precise machining of key aerospace components.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1911276</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1911276</link>
        <title><![CDATA[Friction reduction mechanism of fullerene-containing lubricating oil under boundary lubrication]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wataru Yagi</author><author>Tomomi Honda</author><author>Toshiyuki Tsuchiya</author><author>Tomoko Hirayama</author>
        <description><![CDATA[Fullerenes (FLNs) have attracted attention as lubricant additives because of their antioxidant properties and potential to reduce friction and wear. However, the mechanisms by which FLNs function in lubricating oils remain unclear, particularly with respect to their existence forms in oil and interactions with sliding surfaces. In this study, the tribological properties of FLN-containing oils were investigated in relation to the dispersion state of FLN molecules to clarify the mechanisms underlying their effects on friction and wear. The dispersion state and existence forms of FLNs were characterized using UV-Vis absorption spectroscopy and small-angle X-ray scattering (SAXS), while their interactions with solid surfaces were evaluated using neutron reflectometry and atomic force microscopy. Tribological properties were investigated using reciprocating and ring-on-plate friction tests, and friction under microscopic surface contacts and nanoscale-gap conditions was further examined using a nanoscale-gap friction apparatus. UV-Vis absorption spectroscopy and SAXS revealed approximately 10 nm aggregates in oil containing 1000 ppm FLN. The absence of UV-Vis spectral shifts associated with direct intermolecular contact suggested that these aggregates consisted of FLN molecules coated with hydrocarbon molecules derived from the base oil. Neutron reflectometry and atomic force microscopy indicated negligible adsorption of these aggregates onto solid surfaces. Nevertheless, oil containing 1000 ppm FLN reduced friction and wear, whereas oils containing 10 and 100 ppm FLN increased friction and wear. Additional evaluations showed that the aggregates did not directly reduce friction through rolling or sliding actions. The increased friction and wear at low FLN concentrations were attributed to abrasive interactions of dispersed FLN molecules with the sliding surfaces. In contrast, the friction and wear reduction at 1000 ppm FLN was attributed to enhanced oil-film retention under boundary lubrication conditions rather than direct friction reduction by the aggregates, surface adsorption, or tribochemical reactions. These findings provide new insight into the tribological mechanisms of FLN additives and demonstrate that friction and wear reduction can be achieved through improved oil-film retention without relying on adsorption or tribochemical reactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1919457</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1919457</link>
        <title><![CDATA[Enhanced low-frequency vibration suppression with a active quasi-zero-stiffness absorber]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yaopeng Chang</author><author>Zihao Huang</author><author>Jiaxi Zhou</author><author>Wei Li</author>
        <description><![CDATA[Dynamic vibration absorbers (DVAs) are widely used to significantly reduce vibrations in primary systems. Among these, active dynamic vibration absorbers (ADVAs) stand out due to their ability to suppress vibrations over a broad frequency spectrum. The development of an optimal attenuation strategy for such absorbers under variable ultra-low frequency excitations is crucial for maximizing the performance of the primary system. This paper presents an innovative active quasi-zero-stiffness (AQZS) dynamic vibration absorber (DVA) for effective vibration suppression across ultra-low frequencies. The AQZS DVA utilizes the principle of quasi-zero-stiffness dynamic vibration absorption and is integrated with an electromagnetic actuator to provide tunable vibration attenuation. The theoretical analysis of the AQZS DVA’s restoring force confirms its quasi-zero-stiffness feature. Furthermore, a nonlinear sliding mode control strategy is developed to actively regulate the AQZS DVA, with the Lyapunov stability criterion ensuring convergence of the system. The study systematically examines the effects of various parameters on the vibration attenuation performance of the primary system through a tuning process for the AQZS DVA. Experimental validation of the prototype demonstrates that the nonlinear sliding mode control significantly enhances the vibration suppression efficiency. The findings indicate that the AQZS DVA system is a promising solution for vibration attenuation in the ultra-low frequency range.]]></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.2026.1721474</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1721474</link>
        <title><![CDATA[Contact dynamics modeling of a three-gear system and vibration characteristics analysis of the idler gear]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhiwei You</author><author>Yuankui Luo</author><author>Lixin Xu</author>
        <description><![CDATA[The idler gear is a transitional component in a gear transmission system. By meshing with two non-contacting gears, it changes the rotation direction of the driven gear while maintaining the original transmission ratio. Based on the theories of contact dynamics and multibody dynamics, this paper establishes a contact dynamics model for a three-gear system, which consists of two components: a tooth profile mathematical model and a contact dynamics model. In the tooth profile mathematical model, the meshing phase angle must be calculated to satisfy the initial assembly relationships. In the contact dynamics model, a 9DoF dynamic differential equation for the three-gear system needs to be established, and a contact model for the three-gear system should be constructed based on the mathematical contact judgment method derived from previous research. Based on the established model, this paper investigates the dynamic responses of two-gear and three-gear systems under varying input speeds and load torques. By comparing these results, the influence of the idler gear on the vibration characteristics of the three-gear system is analyzed. The proposed model and findings provide useful reference for the design of idler gears in gear transmission systems.]]></description>
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