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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>2026-09-10T05:01:44.631+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1977529</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1977529</link>
        <title><![CDATA[Correction: Fault diagnosis and prediction of industrial hydraulic pumps based on KOA-CNN-BILSTM model]]></title>
        <pubdate>2026-09-08T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Ting Zhong</author><author>Weiyi Zhu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1883839</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1883839</link>
        <title><![CDATA[Design of a transfer learning fault identification model based on a Markov field and an improved DarkNet]]></title>
        <pubdate>2026-09-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenhao Liu</author><author>Hong Jiang</author>
        <description><![CDATA[To address the challenge of small-sample fault diagnosis for mechanical equipment operating under complex and variable working conditions, this study proposes a comprehensive transfer learning-based diagnostic framework and systematically verifies its effectiveness. First, raw vibration signals are preprocessed to extract 120 statistical features from each sample. These feature vectors are regarded as one-dimensional sequences and transformed into two-dimensional Markov transition field (MTF) images, thereby preserving both temporal transition information and global structural characteristics. The generated MTF images are then fed into a pretrained DarkNet19 network for deep feature extraction. To further enhance feature representation, a gated recurrent unit (GRU) and a multi-head self-attention (MSA) mechanism are incorporated to capture temporal dependencies and emphasize discriminative fault features. In addition, an improved sparrow search algorithm (TLSSA), integrating a t-distribution mutation strategy and Lévy flight, is introduced to adaptively optimize network hyperparameters, improving both convergence efficiency and diagnostic accuracy. The proposed framework is comprehensively evaluated through four aspects: (1) validation of the TLSSA optimization algorithm using benchmark functions; (2) comparative experiments with representative fault diagnosis models to verify the superiority of the proposed framework; (3) ablation studies to quantify the contributions of MTF visualization, transfer learning, GRU, MSA, and TLSSA; and (4) applicability validation on the publicly available Southeast University (SEU) gearbox dataset to evaluate the generalization capability of the proposed framework across different datasets and operating conditions. Experimental results show that the proposed model achieves an overall accuracy of 99.375% on the industrial wind farm dataset and 99.17% on the SEU public dataset, while maintaining rapid convergence, low overfitting, and excellent classification stability. These results demonstrate that the proposed MTF visualization-DarkNet transfer learning framework exhibits strong cross-dataset adaptability, robust generalization capability, and promising engineering potential for intelligent fault diagnosis under complex operating conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1868947</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1868947</link>
        <title><![CDATA[CT-based finite element modeling of a porcine femur: specimen-specific HU–modulus fitting and experimental displacement comparison]]></title>
        <pubdate>2026-09-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Cristian A. Hernández-Salazar</author><author>Octavio A. González-Estrada</author><author>Carlos Amaris</author>
        <description><![CDATA[IntroductionFinite element (FE) models have significantly advanced bioengineering by enabling the characterization of biological tissues, assessment of mechanical responses to loading, and development of prosthetic and biomedical applications. Porcine bone provides a relevant experimental model because of its similarities to human bone tissue. This study investigated the mechanical response of porcine femoral bone by comparing experimental compression data with finite element models derived from medical imaging.MethodsComputed tomography (CT) scans of a porcine femur were segmented to generate FE models with element-wise, piecewise-constant inhomogeneous isotropic material properties assigned from local Hounsfield Unit (HU) averages. Axial compression tests provided experimental load–displacement data, while stress and strain fields were obtained from the numerical models.ResultsThe experimental and numerical displacement values differed by approximately 0.3%, 2%, and 1% for the cortical, cortical–trabecular, and trabecular regional comparisons, respectively. The numerical models additionally enabled characterization of stress and strain distributions and identification of regions of elevated mechanical response.DiscussionThese results demonstrate close specimen-specific agreement between the experimental displacement measurements and the CT-based FE predictions. However, because the HU–density–modulus coefficients were fitted using experimental data from the same femur, the reported agreement represents specimen-specific experimental-numerical consistency rather than independent validation, and transferability to other specimens or imaging protocols remains to be established.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1932727</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1932727</link>
        <title><![CDATA[Integrated longitudinal and lateral coordinated control system for connected and automated vehicles driven by fuzzy control algorithm]]></title>
        <pubdate>2026-09-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yingzhe Luo</author><author>Ahui Niu</author>
        <description><![CDATA[IntroductionConnected and Automated Vehicles face challenges of fixed parameters, poor dynamic adaptability, and the lack of longitudinal and lateral coordination, which affect safe and stable vehicle operations. To solve these problems, this study aims to develop an advanced coordinated control system for intelligent vehicles.MethodsThis study proposes a dynamics modeling technique based on online calibration of connected parameters. This technique integrates the real-time data update patterns of vehicle-infrastructure cooperation to construct an accurate motion model, which combines dynamic parameter inputs to achieve precise evaluation of vehicle driving states. In addition, this study adopts a control technique based on Adaptive Fuzzy Sliding Mode (AFSM) and fuzzy Reinforcement Learning (RL) for coordinated vehicle management and control. This technique takes dynamic states as inputs, enhances the suppression of chattering interference by introducing a fuzzy inference layer, and calibrates the final control results through a dual strategy integrating spatial constraints and adaptive mechanisms.ResultsExperiments are conducted based on a commercial bus. In lateral target tracking control, the model in this study reaches a displacement of 3.78 m at 20 s, and the lateral tracking error drops to −0.02 m, outperforming similar models. In real-vehicle extreme lane-changing control experiments, the root-mean-square lateral error of the model in this study is 0.035 m, while the maximum control chattering rate is only 2.4%. Finally, in longitudinal and lateral coordinated performance analysis, the maximum speed error of the model in this study is 0.25 km/h, and the jerk is 0.12 m/s3, both of which are superior to similar models.DiscussionThe proposed technique demonstrates good application effects in addressing parameter ambiguity and dynamic control imbalance. This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1766180</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1766180</link>
        <title><![CDATA[Digital design of gas turbine engine parts based on multilevel modeling]]></title>
        <pubdate>2026-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nikita Kondratev</author><author>Kirill Romanov</author><author>Matvej Baldin</author><author>Alexey Shveykin</author><author>Mikhail Nikhamkin</author>
        <description><![CDATA[The paper aims at rationalizing the distribution of grain structure parameters across a gas turbine engine (GTE) disk in order to minimize the disk’s mass while ensuring its safe operational conditions. For this purpose, advanced methods of digital design were improved and applied, including state-of-the-art approaches based on multilevel modeling of material structure and properties. The problem was solved using a combined approach. Macro-phenomenological models were employed to determine, during the flight cycle, the evolving fields of stress-strain state and temperature of the entire part. These results were then transferred into a multilevel model to analyze specific regions for the study of strength characteristics, namely, high-temperature strength (resistance to creep and long-term strength), fracture toughness, low-cycle fatigue strength, and thermal stability (resistance to recrystallization and grain boundary migration). Multilevel modeling was based on a comprehensive analysis of the literature data on the structure of the nickel alloy VV751P, as well as on the mechanisms of its deformation and failure. The results of digital design were obtained and analyzed, and recommendations were proposed for rationalizing the material’s grain structure to reduce the disk’s mass while maintaining strength characteristics. The developed approach proved to be effective in digital multilevel design for functionally critical components.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1879675</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1879675</link>
        <title><![CDATA[An empirical investigation of supplier-driven operational risks in manufacturing systems using multivariate statistical analysis]]></title>
        <pubdate>2026-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gajanan Rachalawar</author><author>Ujjwal Mishra</author><author>Sandeep G. Thorat</author><author>Prashant Paraye</author><author>Swapnil Gund</author>
        <description><![CDATA[IntroductionThe outsourcing of manufacturing activities results in operational uncertainties such as supplier delay, instabilities of raw materials, inconsistencies of product quality, cultural difference, regulatory pressure and digital integration issues. There is inadequate empirical research that explores the interrelationship of these operational risks driven by suppliers in the Indian context of outsourcing. The current study focuses on identifying, measuring and categorizing the major operational risks of manufacturing operations driven by suppliers.MethodsA quantitative research approach was utilized for the research. Data were collected from manufacturing professionals involved in supplier coordination, quality control, logistics, production planning and operational management. The responses were analyzed using SPSS through frequency analysis, descriptive statistics, correlation analysis, KMO and Bartlett’s test and exploratory factor analysis. Parallel analysis and Velicer’s MAP test were also utilized.ResultsThe results show that raw material availability was the most critical risk factor with the highest mean value of 4.58, followed by supplier-side disruption at 4.53 and inadequate real-time data sharing at 4.42. Product reliability linked with supplier performance recorded a mean of 4.33 while customer satisfaction decline due to supplier delays recorded a mean of 4.25. Correlation analysis showed strong associations between communication gaps and business ethics misalignment (r = 0.687) and between product specification variation and supplier work practice differences (r = 0.675). The KMO value of 0.861 and Bartlett’s test significance at p < 0.001 confirmed suitability for factor analysis. Parallel analysis and Velicer’s MAP test supported a final three-component solution explaining 52.449% of total variance. The three components were labelled supplier coordination, compliance and performance risk operational quality, regulatory and process-control risk and supply and production-continuity with digital-readiness risk.Discussion and conclusionThe study concludes that supplier-driven operational risk is multidimensional and requires integrated supplier monitoring, digital coordination, quality alignment, compliance tracking and production-continuity planning. The study is limited to questionnaire-based responses. Future research may use longitudinal data, SEM or machine-learning models to validate causal risk pathways.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1894219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1894219</link>
        <title><![CDATA[Design of a vehicle navigation system integrating CART decision tree and DBSCAN algorithm]]></title>
        <pubdate>2026-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yueying Wang</author>
        <description><![CDATA[IntroductionAccurate road type recognition and pavement information aggregation are essential for reliable vehicle navigation in complex urban conditions.MethodsThis paper integrates a CART decision tree with DBSCAN clustering within an edge-cloud architecture. CART classifies roads using 28-dimensional sensor features via Gini gain optimization and post-pruning. DBSCAN (Eps = 30 m, MinPts = 3) aggregates and denoises pavement information points using Haversine distance.ResultsCART achieved 96.8% accuracy, 97.2% precision, 97.3% recall, and a 97.2% F1 score, outperforming RF, SVM, and LR with minimal model size and inference time. DBSCAN merged 85.7% of redundant points with noise below 5%. Real-vehicle tests showed 98% + recognition accuracy for non-ordinary urban roads, a 92.5% detection rate, 89.2% warning success rate, and a 126 m average advance warning distance.DiscussionThe CART-DBSCAN integration provides an effective closed-loop navigation solution from perception to warning. Future work will incorporate multi-modal sensors and domain adaptation to enhance robustness across diverse scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1919085</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1919085</link>
        <title><![CDATA[Explainable deep learning with novel marine domain metrics for oil spill detection]]></title>
        <pubdate>2026-09-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tokula I. Umaha</author><author>Felix Ale</author><author>Ikpaya D. Ikpaya</author><author>John A. Momoh</author><author>Steve A. Adeshina</author><author>Humbulani Simon Phuluwa</author><author>Ilesanmi A. Daniyan</author>
        <description><![CDATA[One of the major challenges faced by marine ecosystem and the environment in general is oil spills especially in oil producing areas or areas with crude oil infrastructure. This threatens aquatic life, render the water body and the environment polluted and unsafe. However, accurate and detection could minimise the impact through a timely and effective response. Though the deployment of deep learning for oil spills detection using synthetic aperture radar (SAR) images, have proved effective, nevertheless, lack of interpretability of artificial intelligence models makes it a black-box which reduces the stakeholders’ trust especially in crucial applications such as environmental monitoring. This study demonstrates the application of explainable artificial intelligence (XAI) specifically the deep learning model for oil spill detection. The model integrates the SpillNet, a customised Convolutional Neural Network (CNN) architecture with five XAI techniques and unique evaluation metrics suitable for marine environmental monitoring were introduced. These include the Marine Domain Relevance (MDR) for the quantification of oil spill, False Positive Analysis (FPA) for look-alike discrimination and Domain Alignment Score (DAS); an expert-based checklist with composite metric. Our comprehensive evaluation of 20 representative samples from 1002 SAR images shows that Gradient-Weighted Class Activation Mapping (Grad-CAM) achieves the highest domain alignment score (0.608 ± 0.074). The proposed SpillNet model also achieved segmentation accuracy (in terms of IoU) of 0.830 (83%) and validation accuracy of 90.5%. Thus, making it the most suitable XAI method for operational oil spill detection systems especially in open-ocean scenarios. The system directly supports several United Nations (UN) Sustainable Development Goals, including the Sustainable Development Goal (SDG) 6 (Clean Water), SDG 7 (Clean Energy), and SDG 14 (Life Underwater), by improving environmental protection through reliable AI-based monitoring systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1900885</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1900885</link>
        <title><![CDATA[Research on flow and pressure drop characteristics of high-flow combined valves under wide operating conditions]]></title>
        <pubdate>2026-08-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fangfang Song</author><author>Zhiqian Feng</author><author>Xu Zhang</author><author>Zhuhai Zhong</author><author>Kunlun Bai</author><author>Xiaodan Zhang</author><author>Jian Song</author>
        <description><![CDATA[High-flow combined valves are critical regulating components in steam turbine systems; their flow capacity, pressure loss characteristics and flow stability across a wide range of operating conditions directly affect the economic efficiency and reliability of the unit. However, the integrated structure of combined valves complicates the throttling jet, separation recirculation and local secondary flow between the upper and lower valves, and the underlying flow mechanisms still require further elucidation. This paper employs a combined approach of numerical simulation and experimental testing to investigate high-flow combined valves under various valve opening and pressure ratio conditions. Given the complex nature of the actual filter screen structure and the difficulty of performing high-precision discretisation directly, a porous medium equivalent model is used to simulate the filter screen. The numerical model was validated using scaled experimental data and total pressure loss characteristics. On this basis, a systematic analysis was conducted of the internal flow patterns, flow regulation characteristics, vortex structure evolution, and energy dissipation patterns within the combined valve. The results indicate that, over a wide range of operating conditions, the Realizable k-ε turbulence model combined with the porous media model can predict the total pressure loss characteristics of the filter screen more accurately than the SST k-ω model. Furthermore, although the filter screen increases the total pressure loss to some extent, it improves the uniformity of the incoming flow, attenuates downstream unsteady fluctuations, and reduces flow entropy generation. This study provides a basis for optimising flow control and designing filter mesh structures in high-flow combined valves.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1922384</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1922384</link>
        <title><![CDATA[Relationships among manufacturing parameters, apparent density, ultimate tensile strength, and post-machining surface roughness of FFF 316L stainless steel parts]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Suleiman Obeidat</author><author>Ali Dinc</author><author>Ali Mamedov</author><author>Murat Otkur</author><author>Kaushik Nag</author>
        <description><![CDATA[This study investigated the relationships among manufacturing parameters, apparent density, ultimate tensile strength (UTS), and post-machining surface roughness of 316L stainless steel parts fabricated by fused filament fabrication (FFF). Specimens were produced using two layer thicknesses, three raster angles, and two build orientations. Following thermal debinding and sintering, apparent density and UTS were measured. The fractured specimens were subsequently micro milled at three workpiece orientations (AngleCut = 0°, 30°, and 45°), and surface roughness was evaluated in three profilometer traverse directions. The experimental data were analyzed using descriptive statistics, Pearson correlation analysis, Type III analysis of variance (ANOVA), multiple linear regression, and regression diagnostics. Pearson correlation analysis revealed negligible to weak associations between apparent density or UTS and surface roughness. After accounting for the manufacturing parameters, regression analysis demonstrated that neither apparent density nor UTS exhibited statistically significant independent associations with post-machining surface roughness. In contrast, workpiece orientation (AngleCut) was identified as the dominant manufacturing factor affecting surface finish, whereas layer thickness exhibited only a marginal influence. Regression diagnostic analyses confirmed that the fitted models satisfied the assumptions of linear regression. Overall, the results indicate that, within the investigated conditions, workpiece orientation during micro milling exhibited the strongest statistical association with post-machining surface quality, whereas apparent density and UTS did not exhibit statistically significant independent associations. These findings provide practical guidance for optimizing hybrid additive–subtractive manufacturing processes to improve surface finish while maintaining desirable mechanical performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1896052</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1896052</link>
        <title><![CDATA[Influence of structural and geometric parameters on the sound absorption performance of carbon fiber–Onyx acoustic panels]]></title>
        <pubdate>2026-08-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ganesh Kekan</author><author>Virendra Bhojwani</author><author>Sachin Pawar</author><author>Daria Derusova</author>
        <description><![CDATA[In acoustic engineering, sound absorption is important in mitigating noise pollution in constructed environments. With the introduction of fused deposition modeling (FDM) based additive manufacturing, it is now more possible to create customizable, lightweight acoustic panels with optimized geometric configuration. This paper examines the acoustic behaviour of 3D-printed perforated polymer composites in terms of the effect of the most important geometrical and physical factors, including perforation ratio, thickness, air gap, layering, and backing conditions on the sound absorption coefficient (SAC). An impedance tube was used to conduct experimental measurements in line with ASTM E1050-19, over the frequency range of 50 Hz–5,000 Hz. The parameters investigated were: perforation ratio of 7.8% and 20.9%, sample thickness of 5–20 mm, air-gap thickness of 10–30 mm, 20 mm PET foam backing, and layered panel configurations. The findings indicated that the SAC improved with the perforation ratio and the sample with 2 mm holes (20.9% perforation ratio) had the highest SAC of 0.735 at 5,000 Hz. The increase in thickness of 10 mm–20 mm resulted in a substantial increase in low-frequency absorption resulting in a maximum SAC of 0.994 at 3,400 Hz. The addition of an air gap shifted the absorption peak to lower frequencies up to 550 Hz as a result of Helmholtz-type resonance, and a 10 mm air gap produced SACs up to 0.92 at 950 Hz. Substitution of air gaps with porous PET foam further extended the bandwidth of absorption to better SAC to 0.9 at 1,200 Hz. The layering of the material was also effective where the split samples exhibited improved absorption in low and high-frequency bands. Also, Delany-Bazley model was applied to predict SAC of solid samples, and it produced a satisfactory fit with an RMSE of 0.1078, particularly at low to mid frequencies. Such results can be used to design tunable acoustic panels that are specifically designed to control broadband noise.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1898911</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1898911</link>
        <title><![CDATA[A review of microstructure optimization design for flow fields of metallic bipolar plates in hydrogen fuel cells]]></title>
        <pubdate>2026-08-26T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Xueming Gao</author><author>Jingsong Tan</author><author>Xiaobo Huang</author><author>Shuanglong Xu</author>
        <description><![CDATA[Metallic bipolar plates (BPPs) are critical components in proton exchange membrane fuel cells (PEMFCs), accounting for the majority of stack mass, volume, and cost. The microstructure of the flow field directly governs reactant distribution, water management, and interfacial contact resistance, thereby determining overall cell performance and durability. This review examines recent advances in the optimization of flow field microstructures for metallic BPPs, encompassing conventional channel configurations, bio-inspired and fractal geometries, and topology-optimized architectures. The analysis extends to manufacturing technologies, including stamping, hydroforming, and additive manufacturing, which dictate the geometric fidelity and cost-effectiveness of metallic plates. Furthermore, protective coating strategies—such as diamond-like carbon, nitride films, and metal oxide layers—are discussed in relation to corrosion resistance and electrical conductivity in acidic fuel cell environments. Multiphysics simulation methods that couple fluid dynamics, electrochemical reactions, and structural mechanics are highlighted as essential tools for rationalizing microstructural design. Finally, future research directions are identified, focusing on multi-objective optimization, in situ characterization, and the integration of advanced manufacturing with intelligent design frameworks to accelerate the commercialization of high-performance PEMFC stacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1817295</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1817295</link>
        <title><![CDATA[Research of a gantry robot for pushing bulk materials loaded in portions into a container]]></title>
        <pubdate>2026-08-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Madi Kaliyev</author><author>Askar Seidakhmet</author><author>Amandyk Tuleshov</author><author>Assylbek Jomartov</author><author>Yerkebulan Tuleshov</author><author>Sayat Makhmet</author>
        <description><![CDATA[IntroductionLoading bulk materials into a container through a small upper hatch results in the formation of a cone-shaped pile that must be redistributed after each portion to ensure uniform filling. This paper presents the design, kinematic modeling, and experimental validation of a 4-DoF gantry robot developed for pushing and redistributing bulk materials inside a container under severe spatial constraints. A specialized end-effector, implemented as a rotatable rod with forward-facing blades, enables planar redistribution of the deposited material.MethodsA complete kinematic model based on the Denavit–Hartenberg method was derived for reliable control of the end-effector position and orientation. Analytical relationships were derived that determine the redistributed layers using geometric methods. Based on these analytical relationships, a redistribution algorithm was developed that takes into account the cone height, layer thickness, material density, and container geometry.ResultsAnalytical relationships describing the geometry of the redistributed layers were obtained, enabling the development of an algorithm for pushing material parallel to the container bottom. A physical prototype was fabricated, and experimental studies were conducted to measure the normal stresses acting on the pusher rod during material displacement.DiscussionThe results confirm the effectiveness of the proposed robot design and control approach in achieving uniform redistribution of bulk material loaded in portions within a constrained container environment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1886046</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1886046</link>
        <title><![CDATA[Fault-tolerant control and safe shutdown of free swing joints of space manipulator based on trajectory optimization]]></title>
        <pubdate>2026-08-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hongwei Hu</author><author>Haoyuan Chen</author><author>Zhan Yang</author><author>Ming Li</author><author>Hong Yin</author>
        <description><![CDATA[IntroductionSpace manipulators are prone to free-swinging joint failures during on-orbit servicing missions, which turn the system from fully actuated to underactuated. Conventional emergency braking strategies result in slow energy dissipation, large base attitude disturbances, and high collision risks, making safe and smooth shutdown difficult to achieve.MethodsThis paper proposes a Trajectory Optimization-based Safe Stopping (TO-SST) method. First, the system with a free-swinging faulty joint is modelled as an underactuated system, where the faulty joint is treated as a passive degree of freedom driven only by dynamic coupling. The torques of the healthy joints are used as control inputs. An optimal control problem is formulated with the objective of minimising kinetic energy dissipation, base attitude deviation, and control effort, while satisfying joint limits, velocity bounds, obstacle-avoidance constraints, and a terminal zero-velocity condition. The Gaussian pseudo-spectral method (GPM) is then employed to globally optimise the motion trajectories of the healthy joints, exploiting the dynamic coupling between active and faulty joints to guide the faulty joint to a smooth stop.ResultsSimulation tests on a planar 3-DOF space manipulator show that, compared with traditional Emergency Braking (EB), Model Predictive Control (MPC), and Adaptive Sliding Mode Control (ASMC), the proposed TO-SST method achieves a 100% success rate over 100 random initial conditions, with an average stopping time of only 4.2 s, a maximum base disturbance within 3.5°, and zero constraint violations. In terms of solver efficiency, GPM has an average solution time of 2.3 s, and its objective function value is 12% lower than that of Sequential Quadratic Programming (SQP) and 18% lower than that of Particle Swarm Optimization (PSO).DiscussionThe results demonstrate that TO-SST effectively harnesses the dynamic coupling between healthy and faulty joints, rapidly dissipating energy while maintaining base stability and strictly satisfying all safety constraints, outperforming the compared strategies. The method offers a practical and efficient solution for emergency response to on-orbit manipulator failures. Future work will extend the approach to high-DOF spatial manipulators and address modelling uncertainties, sensor noise, and multiple concurrent faults.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1910304</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1910304</link>
        <title><![CDATA[Design and implementation of an IoT-Enabled electric motorcycle conversion platform with integrated rider heart-rate monitoring and safety functions]]></title>
        <pubdate>2026-08-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Safaa Gamal</author><author>Mostafa Mousa</author><author>Mazen Mortada</author><author>Mostafa Dahy</author><author>Mostafa Roshdy</author><author>Ziad Abuouf</author><author>Noorhan Abdelgawad</author><author>Naguib Saleh</author>
        <description><![CDATA[The transition from conventional fuel-powered vehicles to electric mobility has become an important strategy for reducing environmental impacts and improving transportation sustainability. This study presents the design, implementation, and experimental evaluation of an IoT-enabled smart platform for converting a conventional TVS HLX 100 motorcycle into an electric vehicle while integrating rider heart-rate monitoring and intelligent safety functions. The conversion process involved replacing the internal combustion engine with a 3000 W brushless DC (BLDC) mid-drive motor powered by a 72 V, 40 Ah lithium-ion battery pack. A motor controller and battery management system were incorporated to ensure efficient power delivery, battery protection, and reliable operation. To enhance vehicle functionality, an Internet of Things (IoT) architecture based on an ESP32 microcontroller was developed for real-time monitoring and communication. The platform integrates battery voltage, current, and temperature sensing, GPS-based tracking, ultrasonic obstacle detection, accident detection, and rider heart-rate monitoring. Operational data are transmitted through a Telegram-based interface, enabling remote supervision of vehicle status and rider heart-rate information. The safety subsystem provides collision warning alerts when nearby obstacles are detected and automatically sends emergency notifications with GPS coordinates in the event of an accident. Furthermore, the physiological monitoring module supports rider safety by continuously monitoring rider heart rate and reducing motor speed when predefined abnormal heart-rate thresholds are detected. Experimental testing demonstrated reliable operation of the propulsion, communication, heart-rate monitoring, and safety subsystems. The developed prototype achieved an overall drivetrain efficiency of approximately 80%, an estimated driving range of 58–82 km per charge, and a charging time of 4–5 h. The proposed platform demonstrates the feasibility of combining electric vehicle conversion, connected heart-rate monitoring, and intelligent safety technologies within a single smart mobility framework.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1835202</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1835202</link>
        <title><![CDATA[Experimental study of the process of thermal friction milling of HARDOX 450 steel and modeling of the temperature in the cutting zone]]></title>
        <pubdate>2026-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karibek Sherov</author><author>Issa Kuanov</author><author>Bakytzhan Donenbayev</author><author>Aibek Sherov</author><author>Medgat Mussayev</author><author>Sayagul Tussupova</author><author>Saule Ainabekova</author><author>Abay Bobeev</author><author>Gulzada Tazhenova</author>
        <description><![CDATA[IntroductionThis study investigates the influence of cutting conditions on the quality of the machined surface during thermal friction milling with pulsed cooling of HARDOX 450 steel.MethodsExperiments were conducted by varying the friction-cutter rotational speed, feed rate, and cutting depth. Surface roughness and hardness were measured after machining. A simplified ANSYS Workbench model consisting of a HARDOX 450 workpiece and a plate representing the cutting edge was used to estimate contact temperatures at nine characteristic points.ResultsThe process provided a surface roughness of Ra = 1.0−7.5 μm. The hardness of the machined surface did not decrease below the characteristic range of HARDOX 450 and could be controlled through the cutting conditions. The rational conditions were n = 2800 rpm, S = 60 mm/min, and t = 2 mm. Numerical results showed a nonuniform temperature field and local heat accumulation near the final part of the contact trajectory.DiscussionThermal friction milling with pulsed cooling can provide controlled surface quality while preserving surface hardness. The numerical temperature values should be regarded as computational estimates; further experimental temperature validation and microstructural analysis are required.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1949338</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1949338</link>
        <title><![CDATA[A review of structural design for variable flux motors in new energy vehicles]]></title>
        <pubdate>2026-08-20T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Yingshuai Liu</author><author>Hengyuan Zhang</author><author>Jianwei Tan</author>
        <description><![CDATA[To provide a comprehensive and balanced perspective, the review also acknowledges well-established mainstream alternatives, including induction motors (IMs), which have demonstrated practical viability in commercial EV applications such as Tesla’s early production models. Variable flux motors (VFMs) have emerged as a transformative technology for new energy vehicle propulsion systems, addressing the fundamental trade-off between low-speed torque capability and high-speed efficiency that constrains conventional permanent magnet synchronous motors (PMSMs). This review systematically examines the structural design aspects of variable flux motors, encompassing hybrid permanent magnet topologies, magnetization state control mechanisms, flux regulation strategies, and electromagnetic optimization methodologies. Particular emphasis is placed on variable flux memory machines (VFMMs) employing low-coercive-force (LCF) magnets such as AlNiCo in combination with high-coercive-force (HCF) neodymium-iron-boron (NdFeB) magnets, as well as novel rotor shifting mechanisms and variable leakage flux designs. The paper synthesizes recent advances in series and parallel magnetic circuit configurations, swiveling magnetization techniques, and multi-objective design optimization frameworks. By analyzing comparative performance metrics across different VFM architectures and identifying persistent technical barriers including magnetization state control precision, demagnetization resistance, and manufacturing complexity, this review aims to provide a comprehensive reference for researchers and engineers engaged in next-generation wide-speed-range electric propulsion system development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1895907</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1895907</link>
        <title><![CDATA[MXene-based protective coatings for mechanical components: architecture design, coupled protection, and interfacial durability]]></title>
        <pubdate>2026-08-19T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Haotian Wu</author><author>Huijie Jia</author><author>Peikai Zhang</author><author>Bangsheng Yin</author><author>Jiawen Tian</author>
        <description><![CDATA[Mechanical components such as bearings, gears, marine shafts, engine-related parts, and aluminum-alloy structures are frequently subjected to coupled wear, corrosion, humidity variation, temperature cycling, and interfacial degradation during service. MXene-dominant and MXene-containing protective coating systems have recently emerged as promising surface-engineering platforms because MXene nanosheets provide two-dimensional layered structures, tunable surface terminations, high aspect ratios, and solution processability that can contribute to low-shear sliding, tribofilm formation, tortuous diffusion pathways, crack deflection, and interfacial modification. This mini review discusses these protective coating systems from a service-oriented perspective, with particular emphasis on architecture design, coupled wear–corrosion protection, environmental stability, and interfacial durability. Recent progress in multilayer, orientation-controlled, polymer-composite, hybrid, environmentally resistant, and smart/self-healing MXene coating architectures is summarized. The main protective mechanisms are analyzed, including layer sliding, tribochemical reorganization, transfer-film formation, maze effects, defect filling, inhibitor release, crack deflection, and coating/substrate adhesion enhancement. Current challenges are also highlighted, including MXene oxidation, restacking, aggregation, coating cracking, delamination, and insufficient validation under realistic coupled service conditions. Future research should move from material-level coating demonstrations toward service-relevant and ultimately service-validated MXene coating systems for bearings, gears, marine shafts, aluminum-alloy structures, and other mechanically loaded components.]]></description>
      </item><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>
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