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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>2026-11-15T03:02:57.967+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1938358</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1938358</link>
        <title><![CDATA[Multi-objective optimization of speed-sensorless sliding mode vector control for electric motors based on HSMO and MOGA]]></title>
        <pubdate>2026-11-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yi Liu</author><author>Jihong Zuo</author>
        <description><![CDATA[PurposeSpeed-sensorless induction-motor drives are limited by low-speed flux-estimation errors, parameter mismatch, and sliding-mode chattering. This study proposes a hybrid sliding-mode observer-based optimized control (HSMO-OC) framework to improve speed estimation, dynamic response, and electromagnetic-output smoothness.MethodsA hybrid sliding-mode observer (HSMO) combines voltage-model and current-model flux estimates using speed-dependent weighting and continuous sliding-mode correction. A multi-objective genetic algorithm (MOGA) jointly optimizes observer and sliding-mode speed-controller parameters under stability and engineering constraints. MATLAB/Simulink simulations of a 2.2 kW induction-motor drive compare four control configurations under startup, speed transitions, load disturbances, low-speed operation, and three uncertainty conditions.ResultsCompared with the conventional SMO-SMC baseline, HSMO-OC reduced speed root-mean-square error from 28.9 to 9.6 r/min, settling time from 0.204 to 0.109 s, and load-induced speed drop from 64.8 to 21.5 r/min. Torque-ripple and current-ripple RMS values decreased from 0.96 to 0.41 N·m and from 1.07 to 0.48 A, respectively. At 100 r/min, speed RMSE was 2.8 r/min. Under resistance mismatch, measurement noise, and inverter dead time, speed RMSE values were 13.4, 12.7, and 14.2 r/min, respectively.ConclusionThe results demonstrate the complementary contributions of hybrid flux observation and joint multi-objective parameter optimization to estimation accuracy, disturbance rejection, and electromagnetic-output smoothness under the tested simulation conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.2014090</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.2014090</link>
        <title><![CDATA[Macroscopic plastic response and atomic-scale dislocation evolution of Ti-6Al-4V alloy during ultrasonic surface rolling]]></title>
        <pubdate>2026-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhenghao Wang</author><author>Zeqi Feng</author><author>Jiakang Zhou</author><author>Xianfu Liu</author><author>Yanhou Liu</author><author>Jinguo Han</author>
        <description><![CDATA[To address the insufficient understanding of the surface plastic deformation mechanism of Ti-6Al-4V alloy during ultrasonic surface rolling processing (USRP), this study adopts an approach combining finite element (FE) simulation and molecular dynamics (MD) analysis. The effects of static load, ultrasonic vibration frequency, and ultrasonic amplitude on residual stress and surface plastic deformation are investigated, and the dislocation evolution and local deformation response under ultrasonic vibration are analyzed at the atomic scale. A three-dimensional explicit dynamic FE model based on the Johnson-Cook constitutive law is established to examine the variations of surface residual stress, equivalent plastic strain, and plastic displacement under different static loads, ultrasonic vibration frequencies, and ultrasonic amplitudes, and the validity of the FE model is verified against experimentally measured residual stress results. The results show that static load is the dominant factor governing the degree of macroscopic surface plastic deformation. Within the investigated static-load range of 100–400 N, the magnitude of the peak residual compressive stress increases overall from 685.11 MPa to 722.09 MPa, although the variation is not strictly monotonic, while the maximum PEEQ increases from 0.12481 to 0.38641 and the affected depths increase markedly. Increasing the ultrasonic amplitude further enhances near-surface plastic deformation, whereas the influence of ultrasonic vibration frequency on macroscopic plastic deformation is relatively limited within the 20–29 kHz range. On this basis, a simplified Ti-Al atomic model of the α phase of Ti-6Al-4V alloy is established to analyze the effect of ultrasonic vibration on atomic-scale plastic deformation behavior by comparing the dislocation evolution, loading force, and system potential energy under conditions with and without ultrasonic vibration. The MD results indicate that periodic excitation is accompanied by an increase in the number and total line length of detected dislocations, together with lower time-averaged indentation reaction forces and higher reported potential-energy values under the investigated simulation conditions. These observations suggest that periodic excitation modifies local defect evolution and indentation response within the simplified atomic model, providing complementary insights into atomic-scale plastic deformation. This study can provide a reference for process optimization and surface strengthening mechanism research of Ti-6Al-4V alloy during USRP.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1979675</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1979675</link>
        <title><![CDATA[Beyond governing-equation residuals: verification and validation of physics-informed neural networks for forward structural-dynamic response prediction]]></title>
        <pubdate>2026-11-12T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Borun Das</author><author>Abir Nag Bulbul</author>
        <description><![CDATA[Physics-informed neural networks are increasingly explored for forward structural-dynamic response prediction because governing mechanics can be embedded directly in learning. Satisfying those equations, however, does not establish that a predicted response is numerically reliable or physically credible. Structural dynamics impose further verification and validation (VandV) demands involving derivative-dependent engineering quantities, long-duration responses, frequency and modal behavior, changing operating conditions, and the provenance of the reference used for comparison. Existing PINN reviews identify many of these issues, but they have not provided a study-level audit of how the corresponding evidence is distributed across published forward structural dynamic studies. This Mini Review addresses that gap. Drawing on established computational mechanics VandV practice, we define four evidence dimensions: equation and numerical consistency, structural response verification, dynamic fidelity, and predictive scope with benchmark provenance. Each dimension is resolved into categories with explicit decision rules. These rules are then applied through a full-text review of a delimited study corpus. Each dimension is tied to measurable evidence and to a reporting chain connecting the quantity of interest, the error metric, the reference and its uncertainty, the intended-use tolerance, the observed error, and the resulting VandV conclusion. The resulting evidence map shows that validation coverage remains uneven and that evidence is rarely accumulated across dimensions within individual studies. Worked applications further show that the dimensions complement rather than substitute for one another. Credible forward structural-dynamics PINNs therefore require mechanics-relevant, purpose-specific validation beyond residual minimization or isolated agreement in displacement.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1979480</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1979480</link>
        <title><![CDATA[Machine learning condition monitoring systems for rotating machinery fault detection]]></title>
        <pubdate>2026-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhamad Mursyied Mahazani</author><author>Muchamad Oktaviadri</author><author>Nafrizuan Mat Yahya</author><author>Ahmad Razlan Yusoff</author>
        <description><![CDATA[The vibration-severity alarms that dominate industrial practice indicate that a rotating machine has deteriorated, not what has gone wrong. This laboratory proof-of-concept study examines whether a machine learning pipeline can identify the mechanism and estimate the imposed severity of seeded faults from multi-axis vibration data, and whether it can operate within an Industrial Internet of Things architecture. An unloaded rig comprising a 0.37 kW induction motor driving a 20 mm shaft on 6204 ZZ bearings was instrumented with a piezoelectric accelerometer on the motor housing and a tri-axial MEMS accelerometer on the bearing housing, sampled at 25.6 kHz. Ten machine states (a healthy baseline and three severity levels each of bearing contamination, mass unbalance and shaft misalignment) were recorded on three assembly replications, giving 30 recordings, which are the independent observations, segmented into 3,570 overlapping one-second windows. Sixty descriptors were reduced to eighteen, and six classifiers were compared under nested leave-one-replication-out validation with recording-level partitioning. Gradient-boosted trees achieved a window-level accuracy of 98.9% (fold-level 95% confidence interval 97.9%–99.9%, indicative only), a macro-averaged F1 score of 0.988 and a Matthews correlation coefficient of 0.985, and classified all 30 recordings correctly by majority vote. A one-dimensional convolutional network reached a mean accuracy of 99.1%; the two models were not compared statistically. An exploratory univariate RMS-threshold rule applied to the recorded instrument output classified only 3 of 9 healthy sets correctly, because the campaign-specific healthy baselines spanned a sevenfold amplitude range; this failure does not depend on the uncalibrated scale of the bearing channel. Whether campaign-specific characteristics contribute to fault-family discrimination has not been excluded, and the highest-ranked feature lies beyond the rated bandwidth of the bearing sensor. The pipeline executed in 11.3 ms per window on a laptop-class processor and delivered results to a ThingsBoard dashboard with an approximate median end-to-end latency of 1.4 s and no loss of classification records. These results indicate laboratory feasibility of fault-type classification on a single unloaded rig at one speed; generalisation to industrial machines, other operating conditions and lower-cost acquisition hardware was not tested.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1965238</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1965238</link>
        <title><![CDATA[Thermal–hydraulic performance of transverse ridge geometries in water-cooled microchannel heat sinks]]></title>
        <pubdate>2026-11-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joseph Marcello</author><author>Sumith Yesudasan</author>
        <description><![CDATA[IntroductionLiquid-cooled microchannels offer an effective approach to removing high heat loads from compact electronic systems. Although surface modifications can enhance heat transfer, their benefits must be evaluated against the accompanying hydraulic penalties. This study investigates the influence of transverse ridge geometry on the thermal and hydraulic performance of a water‐cooled copper microchannel.MethodsTwo-dimensional conjugate heat-transfer simulations were performed using COMSOL Multiphysics to compare semicircular, triangular, and rectangular transverse ridges. Ridge height (e = 0.05–0.15 mm), pitch (ps = 0.25–0.50 mm), and Reynolds number (Re = 100–1000) were systematically varied, resulting in 135 ridged‐channel simulations and five smooth-channel reference cases. Thermal and hydraulic performance were evaluated using the Nusselt number, pressure drop, thermal performance factor (TPF), and a Pareto analysis of heat‐transfer enhancement versus reference-width pumping power.ResultsAt Re = 1000, e = 0.15 mm, and ps = 0.40 mm, the smooth channel yielded a Nusselt number of 14.51, compared with 16.83, 17.98, and 23.47 for the semicircular, rectangular, and triangular ridges, respectively. These values correspond to heat-transfer enhancements of 16.0%, 23.9%, and 61.8%. Triangular ridges provided the greatest thermal enhancement but incurred the highest pressure-drop penalty. The maximum TPF of 1.37 was achieved with triangular ridges at Re = 1000, e = 0.15 mm, and ps = 0.50 mm, which also yielded the highest Nusselt number of 24.01. Pareto analysis indicated that rectangular ridges occupied much of the lower‐pumping‐power frontier, while triangular ridges provided superior heat‐transfer enhancement at higher pumping demand.DiscussionRidge geometry, height, and pitch collectively influence microchannel thermal‐hydraulic performance. Triangular ridges are favorable when maximizing heat transfer is the primary objective, whereas rectangular ridges offer competitive performance under tighter pumping-power constraints. These findings highlight the importance of considering hydraulic penalties alongside thermal enhancement when designing ridge‐enhanced microchannel heat sinks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1960499</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1960499</link>
        <title><![CDATA[A comparative analysis of magnetohydrodynamic jeffery-hamel blood flow: solutions from HPM to ANN]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nathiya M</author><author>Padma R</author>
        <description><![CDATA[Studying magnetic effects on blood flow through geometrically varying arteries reveals how vessel shape influences circulation. However, it is difficult to find accurate and efficient solutions for these complex flow patterns. In this study, we look at magnetohydrodynamic Jeffery-Hamel blood flow in an arterial channel by combining analytical solutions, numerical checks, and neural network predictions. We solve the main equation using the Homotopy Perturbation Method (HPM) and compare these results with numerical solutions from the BVP5c method, finding that they match well. Using more HPM iterations makes the results more accurate and stable. We then use the data to train a neural network time-series model with the Levenberg-Marquardt algorithm. Bayesian Regularization gives the best predictions, with low error and strong agreement with the reference data. This combined approach offers an efficient way to analyze and predict complex blood flow in arteries under magnetic fields.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1897016</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1897016</link>
        <title><![CDATA[Industry 4.0 in Bahrain: policy architecture, implementation mechanisms, and manufacturing readiness challenges]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhamed Abdelhamed Bakhet Abdallah</author>
        <description><![CDATA[Industry 4.0, or the Fourth Industrial Revolution, denotes the integration of cyber-physical systems, the Internet of Things, artificial intelligence, big-data analytics, additive manufacturing, and advanced robotics into manufacturing and industrial processes. For small, hydrocarbon-reliant economies such as the Kingdom of Bahrain, the adoption of these technologies is closely linked to economic diversification, fiscal sustainability, and private-sector employment objectives. This study adopted a qualitative policy-analysis design based on desk research and structured document review, supported by a limited comparative benchmarking exercise across selected Gulf Cooperation Council (GCC) states. The analysis drew primarily on official Bahraini policy materials, including the Ministry of Industry and Commerce (MoIC), the Industrial Sector Strategy 2022–2026, the iFactories national initiative, the Smart Industry Readiness Index (SIRI), Tamkeen, and the Bahrain Economic Development Board (EDB), and triangulated these materials with selected academic and institutional sources. The paper examined how Bahrain’s Industry 4.0 policy architecture was structured, which institutions and instruments supported implementation, how the approach compared with selected GCC initiatives, and which constraints affected implementation, particularly for small and medium-sized enterprise (SME) manufacturers. The analysis found that Bahrain had developed an institutionally coherent policy framework anchored in iFactories and SIRI-based factory-readiness assessment, but publicly available evidence on implementation outcomes, including productivity, exports, employment, and environmental performance among participating factories, remained limited. Accordingly, the study distinguished between policy design, policy targets, expected benefits, illustrative examples, and verified outcomes, and treated sustainability as a policy linkage rather than a measured outcome. The principal contribution was a structured policy analysis of Bahrain’s manufacturing-readiness agenda and identification of the evidence gaps that future empirical evaluation should address. Analytically, the study was guided by a framework combining industrial-policy theory, absorptive-capacity theory, and Industry 4.0 readiness-assessment approaches, operationalised through seven analytical categories: policy architecture, implementation mechanisms, readiness assessment, financing and support instruments, SME absorptive capacity, sustainability linkage, and monitoring and evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1987464</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1987464</link>
        <title><![CDATA[Location and path planning of dorsal acupoints for a robotic massage system]]></title>
        <pubdate>2026-11-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bingqian Zou</author><author>Mengxia Luo</author><author>Yujin Wang</author>
        <description><![CDATA[In order to address the problems associated with manual acupoint massage, a novel integrated robotic dorsal acupoint massage system that combines visual acupoint localization with massage path planning is proposed in this paper. In this massage system, computer vision is employed to locate the dorsal acupoints positions which are then used for robotic path planning, followed by the execution of massage operations via robot end-effector. Methodologically, the primary contributions of this study are outlined as follows. Firstly, an adaptive brightness mapping data augmentation strategy for the training phase that can generate non-uniform illumination data, is proposed to mitigate illumination distribution discrepancies between experimental and real-world environments. Secondly, a state-space network equipped with a flexible multiplicative gating mechanism is developed to enable global receptive field modeling and enhance feature selection capability while maintaining linear computational complexity. Thirdly, a structural alignment loss is put forward to constrain the relative geometric relationships among keypoints, thereby suppressing structural distortion and improving acupoint detection accuracy. Fourthly, a quality-aware adaptive local reconstruction mechanism has been incorporated into the standard Ant Colony Optimization with the objective of generating shorter and smoother acupoint massage paths without additional priors. In this study, the visual acupoint localization and massage path-planning modules are sequentially integrated, enabling the detected acupoint coordinates to directly serve as the input for subsequent robotic path planning. A comparison of the proposed system with representative related works reveals that it achieves higher accuracy and stability in acupoint location and significantly improves path planning quality, demonstrating excellent practical performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1963728</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1963728</link>
        <title><![CDATA[Double-pulse diesel injection strategy on combustion performance and emission characteristics of turbocharged multi-cylinder CRDI dual-fuel diesel engine fueled with gasoline/diesel and ethanol/diesel]]></title>
        <pubdate>2026-11-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Naasam Vasukumar</author><author>Nataraj Ganesan</author>
        <description><![CDATA[Rising environmental regulations and increasing fossil fuel costs have intensified the search for sustainable and renewable fuel alternatives. The associated well-to-tank and tank-to-wheel emissions from fuel production, transportation, and end-use further highlight the need for cleaner energy sources. Compared to spark ignition engines, compression ignition engines are more durable and have superior thermal efficiency. Due to the autoignition of heterogeneous mixtures, diesel engines have significant issues with high amounts of nitrogen oxide and particulate matter emissions. The present work focuses on the utilization of low-reactivity gasoline/ethanol in a turbocharged multi-cylinder common rail direct injection engine operating in conventional dual-fuel mode. In the first phase, the engine was operated with pure diesel injection timing of 23° bTDC as reference across four brake mean effective pressure (BMEP) loading percentages: 25%, 50%, 75%, and 100%, corresponding to low load at 3 bar, a mid load at 6.1 bar, a mid-high load at 9.07 bar, and a high load at 11.43 bar. In the second phase, port-injected gasoline and direct-injected diesel fuel were supplied using a double-pulse injection strategy: 30° pilot/5° main injection as (S1) and 40° pilot/10° main injection as (S2). In the third phase, an ethanol PFI/diesel double-pulse diesel injection (DPI) strategy was employed to evaluate combustion characteristics, including in-cylinder pressure, net heat release rate, rate of pressure rise, mean gas temperature, and cumulative heat release rate. Engine performance parameters, including brake power (BP), brake thermal efficiency (BTE), and indicated thermal efficiency, along with regulated emissions such as carbon dioxide (CO2), carbon monoxide (CO), unburned hydrocarbons, and nitrogen oxides (NOx), were also evaluated. Results reveal that port-injected low-reactivity fuels enhanced mixture homogeneity and increased the premixed energy share compared to baseline diesel operation. Overall, nitrogen oxide (NOx) emissions for GPI/DDI under low and mid load operating conditions decreased by 89.94% and 29.50% compared to EPI/DDI. When the load rises from mid to high load, the EPI/DDI has enhanced differences of 8.63%, a 149.14% reduction compared to GPI/DDI. Smoke opacity decreased significantly by 10.75% at high load, attributed to ethanol’s high oxygen content.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1817499</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1817499</link>
        <title><![CDATA[Free vibration behavior of bio-inspired helicoidal laminated sandwich skew plates: finite element-assisted higher-order zigzag theory]]></title>
        <pubdate>2026-11-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ankit Singh</author><author>Aman Garg</author><author>Vaishali Sahu</author>
        <description><![CDATA[IntroductionHelicoidal (Bouligand) architectures found in natural biological systems have attracted considerable attention for enhancing the mechanical performance of laminated composite structures without increasing material usage or weight. Although the static and impact behavior of helicoidal laminates has been extensively investigated, studies on the free vibration characteristics of bio-inspired helicoidal laminated sandwich skew plates remain limited. This study aims to evaluate the dynamic performance of cross- and double‐helicoidal laminated sandwich skew plates and compare their vibration behavior with conventional cross‐ply laminates.MethodsThe free vibration behavior of bio‐inspired cross‐helicoidal and double‐helicoidal laminated sandwich skew plates was analyzed using a finite element formulation based on higher‐order zigzag theory. The governing equations were derived through Hamilton's principle. Skew geometry was rigorously incorporated using Jacobian‐based coordinate transformations and boundary transformation matrices. A comprehensive parametric investigation was performed to assess the effects of skew angle, side‐to‐thickness ratio, core thickness ratio, interply angle, and boundary conditions on the natural frequencies of the structures.ResultsThe results indicate that helicoidal layup configurations significantly enhance the dynamic stiffness of laminated sandwich skew plates compared with conventional cross‐ply laminates. Among the configurations examined, the cross‐helicoidal CH12 arrangement exhibited the highest vibration performance, achieving up to 14.7% higher natural frequencies than cross-ply laminates under simply supported boundary conditions (a/h = 100, skew angle = 0°). Under cantilever boundary conditions, frequency improvements of approximately 9–10% were observed. Furthermore, increasing the skew angle generally increased the structural stiffness and natural frequencies for most boundary conditions. However, cantilever plates demonstrated a non-monotonic response due to the interaction between geometric effects and stiffness variations.Discussion and ConclusionsThe study demonstrates that bio‐inspired helicoidal architectures provide superior vibration characteristics and enhanced dynamic stiffness relative to conventional laminate designs. The observed improvements are attributed to the more efficient distribution of fiber orientations, which enhances structural rigidity and resistance to deformation. These findings highlight the potential of helicoidal laminated sandwich skew plates for lightweight engineering applications requiring improved vibration resistance and dynamic performance, such as aerospace, marine, and advanced structural systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1910151</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1910151</link>
        <title><![CDATA[Multi-agent path planning and reinforcement learning for wire harness handling]]></title>
        <pubdate>2026-11-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ludwig Streloke</author><author>Marco Peyerl</author><author>Jörg Franke</author><author>Patrick Bründl</author>
        <description><![CDATA[Wire harness manufacturing remains one of the most labor-intensive parts of automotive production, with the “form and tape” assembly process constituting the key bottleneck. Emerging industrial concepts shift automation from single-wire routing to module-based forming. This leads to a multi-agent planning and control problem in which moving connector housing fixtures are strongly coupled by the partially formed harness. The contact-rich and highly variable behavior of the wires as linear deformable objects is difficult to model analytically. We investigate two planning and control paradigms within a MuJoCo-based simulation environment with a Gymnasium-compatible interface. First, we use multi-agent path planning ideas on continuous, cable-coupled motion via prioritized planning with heuristic guidance and reactive constraint checking, yielding a 78% overall success rate for the best planner configuration. Second, we apply reinforcement learning, both standalone and as a residual on top of a geometric base policy, comparing SAC, PPO, and TRPO. Surprisingly, the best results are achieved by plain end-to-end SAC, which reaches a mean overall success rate of 98% (range: 95%–99%) and solves nearly 114 of 120 complete target permutation, against 65 for the best planner, whereas residual RL, despite its greater stability on single transitions, fails to generalize to the full sequence of target configurations. The results indicate a central trade-off between rule-based algorithmic planning and learned control in wire harness handling. The simulation environment is publicly available at: https://github.com/ludwigstr/WireHarness-MultiAgent-RL.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1937227</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1937227</link>
        <title><![CDATA[BEV-based collaborative distillation and multi-domain transformer fusion for autonomous driving 3D object detection]]></title>
        <pubdate>2026-11-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shujian Zhang</author><author>Subing Liu</author>
        <description><![CDATA[IntroductionAutonomous driving systems in complex traffic environments require accurate detection of 3D objects, such as vehicles, pedestrians, and cyclists.MethodsTo address challenges such as unstable image depth, sparse Light Detection and Ranging (LiDAR) point clouds, and insufficient cross-modal fusion, this study proposes a Bird’s Eye View (BEV)-based Instance-Context Collaborative Distillation and Multi-domain Transformer Fusion (BICD-MTF) architecture. First, multi-view image features and LiDAR point cloud features are mapped into a unified BEV space. Next, instance structural relationship guided distillation and BEV context response collaborative distillation are employed to enhance cross-modal consistency. Finally, multi-domain collaborative attention fusion and a Transformer decoder are employed to achieve robust 3D object detection.ResultsExperiments show that the mean average precision and nuScenes Detection Score (NDS) of the complete method reach 69.09% and 72.13%, respectively, which represent improvements of 4.41% and 3.77% over the baseline model. Meanwhile, the model achieves improved localization and motion estimation accuracy, with the mean translation error, mean orientation error, and mean velocity error reduced to 0.284 m, 0.312 rad, and 0.254 m/s, respectively. When the detection distance reaches 100 m, the mean average precision remains at 56.94%, and the NDS maintains at 65.28%. In a continuous 300-frame sequence from the nuScenes validation set, the NDS remains stable between 72.03% and 72.38%.DiscussionThe results indicate that the proposed method improves the accuracy and stability of 3D object detection under nuScenes-like urban driving scenarios and provides a useful reference for multi-source perception fusion in autonomous driving.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1986327</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1986327</link>
        <title><![CDATA[Data-driven digital twin-assisted energy storage scheduling for electric vehicle charging stations under demand uncertainty: explainable forecasting and multi-objective dispatch]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhengyang Fang</author>
        <description><![CDATA[ObjectivesPublic electric-vehicle charging sites are often planned from transaction records without charger telemetry or an installed battery energy storage system (BESS). This study evaluates whether such data can support pre-deployment storage scheduling under uncertain demand.MethodsWe analyzed 441,077 charging transactions from 13 sites in Jiaxing, China. After prespecified screening, 348,404 sessions (6.754 GWh) were reconstructed into 30-min station demand. LightGBM forecasting was evaluated on an untouched October–December 2021 test period across 12 stations. Additional analyses included station-day cluster bootstrap, station-wise validation, an independently trained median-quantile model, station-conditional split-conformal calibration, and energy-conserving reconstruction sensitivity. An offline decision twin evaluated a 79.15 kW/158.31 kWh virtual BESS at Bus Station.ResultsLightGBM achieved RMSE 29.37 kW and R2 = 0.647, compared with 29.86 kW for the seven-day same-slot baseline. A 10,000-resample cluster bootstrap estimated an RMSE improvement of 0.493 kW (95% CI 0.287–0.701), equivalent to 1.65% (0.96%–2.34%). LightGBM improved RMSE at 10 of 12 stations. The original q05–q95 interval covered 79.56% of test observations; split-conformal calibration increased coverage to 89.03% with mean width 76.86 kW. The independently trained median model was highly correlated with, but not equivalent to, the point model (r = 0.992; RMSE 30.17 vs. 29.37 kW). Reconstruction sensitivity changed cost and peak reductions by at most 0.03 and 0.08 percentage points, respectively. Uncertainty-aware dispatch reduced modeled expected cost by 3.31% and q95 peak demand by 3.14%.ConclusionThe results support a modest but stable forecasting gain, reveal station heterogeneity, and indicate limited sensitivity to the tested reconstruction assumptions. All storage benefits remain conditional simulation outcomes from an offline pre-deployment decision model.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.2006613</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.2006613</link>
        <title><![CDATA[Fatigue life prediction of variable-load industrial robotic arms based on physics-informed graph neural networks]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jia Guo</author>
        <description><![CDATA[IntroductionIndustrial robotic arms operate under long-term variable-load conditions, and joint fatigue accumulation may lead to sudden failures. Conventional life-prediction methods cannot effectively integrate spatial topology, temporal degradation rules and fatigue physical mechanics. This study proposes a physics-informed Graph Attention-GRU (PI-GAT-GRU) model for fatigue-life prediction of variable-load industrial robotic arms.Methods Robotic-arm joints are mapped into a topology-mechanics graph. Spatiotemporal features are extracted via dynamic graph attention and GRU units. The Miner damage criterion and stiffness-degradation model are embedded, and a physics-regularized joint loss function is constructed for end-to-end fatigue-life prediction. Comparative experiments against three baseline models and ablation studies are conducted on multi-body simulation datasets covering low, medium and heavy-load conditions.ResultsThe subset-level prediction accuracy of PI-GAT-GRU reaches 96%–97% under low-load, 94%–97% under medium-load, and 93%–95% under heavy-load conditions. Its overall average accuracy on the mixed full-condition test set is 92.48%. Benchmark models achieve 91%–94% accuracy under low-load and drop to 88%–92% under heavy-load conditions. The proposed model obtains better accuracy and robustness under variable-load scenarios.DiscussionThe PI-GAT-GRU realizes effective fusion of data-driven learning and fatigue physical constraints. It supplies reliable technical support for predictive maintenance and health management of industrial robotic arms. Further work will focus on cross-platform validation and nonlinear-damage mechanism integration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.2006059</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.2006059</link>
        <title><![CDATA[Influence of thermal history on microstructural evolution and semi-solid processability of AZ91D magnesium alloy]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Javier Antonio Navas</author><author>Sergi Menargues</author><author>D. Gutierrez</author><author>E. Rua Ramirez</author><author>Isabel Espinosa</author><author>Josep A. Picas</author>
        <description><![CDATA[Semi-solid processing of AZ91D magnesium alloy depends on the thermal history preceding deformation, which influences the evolution of the solid–liquid microstructure and the subsequent processing response. This study examines the integrated thermal route comprising homogenization, partial remelting, induction reheating, and semi-solid extrusion. The as-cast alloy was homogenized at 455 °C for 12 h and subjected to partial-remelting treatments at 560 °C, 575 °C, and 585 °C for 10 and 20 min. The evolution of the primary α-Mg phase was characterized by optical microscopy and quantitative image analysis using equivalent diameter and shape factor. Additional microstructural changes were evaluated after induction reheating to 560 °C. Semi-solid extrusion was performed at billet temperatures of 500 °C–560 °C and ram speeds of 3.33–16.66 mm·s−1, with maximum extrusion pressure and product integrity used as indicators of processability. Partial remelting modified the α-Mg morphology and promoted liquid-phase formation. At 560 °C and 575 °C, both equivalent diameter and shape factor increased with holding time, indicating simultaneous particle coarsening and changes in particle shape. Subsequent induction reheating produced further qualitative changes in the morphology and spatial arrangement of the primary α-Mg phase, showing that the microstructure formed during partial remelting was not retained unchanged before extrusion. The macroscopic extrusion response was governed predominantly by billet temperature and ram speed, whereas the preceding billet condition showed no measurable systematic effect on maximum extrusion pressure. Increasing ram speed reduced extrusion pressure but was accompanied by product failure at the highest speeds investigated. Sound products were obtained at ram speeds of 3.33–8.33 mm·s−1, whereas product breakage occurred at higher speeds. A quadratic regression model fitted to the 27 experiments that produced sound products yielded R2=0.867, an adjusted Radj2=0.835, an in-sample RMSE of 36 MPa, and an MAE of 23 MPa. Leave-one-out cross-validation of the fixed model form yielded QLOO2=0.821 and an RMSE of 42 MPa. Overall, the results establish an integrated framework for examining the relationships between thermal history, semi-solid microstructural evolution, and processability in AZ91D magnesium alloy. They also highlight the need to assess extrusion pressure and product integrity separately when defining the processing window.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1952180</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1952180</link>
        <title><![CDATA[A review of cylindrical interfacial instability in viscous and Rivlin–Ericksen viscoelastic fluids under multiphysics effects]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Kowsalya C</author><author>Pankaj Shukla</author>
        <description><![CDATA[Interfacial instability is important in many engineering and industrial applications involving viscous and viscoelastic fluid systems. This review systematically examines Kelvin–Helmholtz, Rayleigh–Taylor, capillary, and cylindrical instabilities, with particular emphasis on Rivlin–Ericksen viscoelastic fluids, based on a systematic search of seven databases covering publications from 1992 to 2026, from which 285 of 1,256 identified studies met the inclusion criteria. The influence of heat transfer, mass transfer, porous media, electric fields, and rotating cylindrical configurations on interfacial stability is critically discussed, and the governing equations, constitutive models, dispersion relations, and stability criteria reported in the literature are systematically summarized and compared. The reviewed studies show that viscosity, viscoelasticity, thermal transport, mass diffusion, and external body forces significantly influence disturbance growth and interfacial stability. A key finding of this review is that the vast majority of the reviewed literature including nearly all Rivlin–Ericksen fluid studies relies exclusively on analytical approaches, such as linear stability theory, normal-mode analysis, perturbation methods, and viscous potential flow formulations, which are restricted to small-amplitude disturbances and idealized geometries. Advanced computational techniques capable of resolving nonlinear, large-amplitude, and geometrically complex interfacial behavior including the Finite Volume Method (FVM), Finite Difference Method (FDM), Finite Element Method (FEM), and Boundary Element Method (BEM) remain almost entirely unexplored for this class of problems. This review identifies that gap as the principal barrier to progress in the field and outlines a path toward closing it, proposing future work centered on nonlinear analyses, coupled multiphysics models, and efficient FVM/FDM/BEM-based numerical simulations of complex viscoelastic interfacial flows.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1997420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1997420</link>
        <title><![CDATA[Pose-conditioned and uncertainty-aware scene representation for visual relocalization]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Ye</author><author>Ruizeng Wei</author><author>Chang Jiang</author><author>Dawei Peng</author><author>Lei Wang</author>
        <description><![CDATA[Visual relocalization requires compact scene representations that capture global spatial context while preserving local geometric precision. Existing regression-based methods often emphasize one aspect at the expense of the other. Global image representations encode scene-level spatial information but provide limited geometric accuracy, whereas locally regressed scene coordinates preserve explicit 2D–3D geometry yet remain vulnerable to visual ambiguity and unreliable predictions. We propose a pose-conditioned and uncertainty-aware scene representation framework that integrates these complementary cues within a unified relocalization pipeline. Global pose supervision is used to learn a pose-aware image representation that conditions local features for scene-coordinate regression while preserving their spatial structure. Scene coordinates are further modeled with heteroscedastic uncertainty to characterize spatially varying prediction reliability. The estimated uncertainty is propagated into geometric pose estimation to guide correspondence sampling, pose-hypothesis evaluation, and inlier selection. On the 7-Scenes benchmark, the proposed method achieves an average localization success rate of 88.5% under the 5cm/5° criterion with a compact 47 MB model. Qualitative analysis further suggests that high-uncertainty predictions are often associated with geometrically inconsistent scene coordinates, supporting the use of uncertainty as a reliability cue for pose estimation. These results demonstrate the effectiveness of jointly exploiting pose-aware global context, local geometric correspondence, and predictive uncertainty for compact visual relocalization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1975352</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1975352</link>
        <title><![CDATA[Advances in 3D-printed acoustic panels: experimental investigations, numerical modelling, and machine learning applications]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ganesh Kekan</author><author>Virendra Bhojwani</author><author>Sachin S. Pawar</author><author>Tanmay Deshpande</author>
        <description><![CDATA[Additive manufacturing provides a flexible route for fabricating acoustic panels with controlled porous, lattice, micro-perforated, resonant, and metamaterial architectures that are difficult to realize using conventional manufacturing. This review combines a bibliometric analysis of Scopus-indexed literature spanning 1980–2026 with a critical synthesis of relevant experimental, analytical, numerical, and machine-learning studies addressing the design, fabrication, modelling, and acoustic performance of 3D-printed structures. Across the reviewed literature, porosity, cell topology, perforation geometry, cavity depth, layer thickness, lattice orientation, airflow resistance, and printing parameters repeatedly emerge as key factors governing sound absorption, particularly in the low- and mid-frequency ranges. Empirical models remain useful for rapid assessment when limited material information, especially airflow resistivity, is available, whereas JCA-type equivalent-fluid models provide a more physically representative description when reliable microstructural transport parameters are known. Transfer-matrix and resonator-based analytical methods are computationally efficient for layered and geometrically regular systems, while finite-element and other numerical approaches are better suited to complex geometries, manufacturing imperfections, and coupled vibro-acoustic behaviour, although at higher computational cost. Machine-learning methods show potential for rapid forward prediction, optimization, and inverse design, but their wider use is currently constrained by limited standardized datasets and experimental validation. The reviewed evidence also shows that additive manufacturing can improve geometric tunability and, in selected designs, reduce material usage or structural mass; however, broader claims of superior performance or sustainability remain application-dependent and should consider fabrication energy, material durability, recyclability, and end-of-life treatment. Major barriers to practical implementation include manufacturing-induced dimensional deviations, inconsistent testing protocols, limited diffuse-field and full-scale validation, material durability, multifunctional design requirements, production scalability, and cost. Future progress therefore requires integrated experimental–numerical–data-driven frameworks, standardized validation procedures, sustainable material selection, and scalable manufacturing strategies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1961950</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1961950</link>
        <title><![CDATA[Numerical investigation of fin-enhanced LaNi5 metal hydride reactors for improved hydrogen absorption and desorption]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>C. Ranjith</author><author>A. Satheesh</author>
        <description><![CDATA[In this study, metal hydride reactors with differing fin configurations were investigated for hydrogen absorption and desorption processes. The simulations were performed using COMSOL Multiphysics V 6.3. To model the coupled heat and hydrogen transfer phenomena, heat transfer in porous media, Brinkman, and partial differential equation physics interfaces were employed. Metal hydride reactors with three geometrical configurations were considered: a reactor without fins, a reactor with fins 10 mm long and 2 mm thick attached at the center, and a reactor with fins of the same dimensions attached at the outer surface. The overall dimensions of the reactor were 50 mm in diameter and 80 mm in height. An expansion volume of 20 mm height was provided above the metal hydride bed and metal hydride of LaNi5 was filled up to 60 mm height, and hydrogen was supplied at the top of the expansion volume. The heat and hydrogen transfer rates within the metal hydride bed were analyzed for different heat transfer fluid temperatures ranging from 293 K to 313 K. The results indicate that the outer-fin configuration provides the most effective enhancement of hydrogen absorption, exhibiting an absorption time of 1,140 s. In comparison, the inner-fin configuration requires 2,080 s, while the no-fin configuration takes 2,820 s to complete the absorption process. Thus, the use of inner fins reduces the absorption time by approximately 26.24%, whereas the outer-fin configuration achieves a substantially greater reduction of 59.57% relative to the no-fin configuration. These results demonstrate the superior effectiveness of the outer fin arrangement in accelerating hydrogen absorption.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1979049</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1979049</link>
        <title><![CDATA[Expansion force and pressure sensing for mechanics-aware battery management in electric vehicles: from state estimation to early safety warning]]></title>
        <pubdate>2026-10-30T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Feng Li</author><author>Huaying Qiao</author><author>Lihua Jiang</author><author>Xianzheng Liu</author><author>Yuan Li</author><author>Jiayi Li</author>
        <description><![CDATA[Conventional battery management systems (BMSs) for electric vehicles rely primarily on voltage, current, and surface temperature, potentially overlooking mechanical signatures of degradation and incipient failure. This mini review examines expansion force and pressure sensing as complementary observables for mechanics-aware battery management. We first relate reversible swelling, irreversible deformation, gas generation, and casing response to electrochemical and thermal processes. Established effective-stiffness approximations are then integrated into a review-level framework linking free cell expansion to constrained force and distinguishing SOC-dependent reversible behavior, SOH-related baseline drift and hysteresis, gas-related anomalies, thermal effects, and viscoelastic relaxation. Published studies report SOC estimation errors of approximately 3% or lower using electrical–mechanical sensor fusion and SOH errors below 1.5% in representative datasets. Under specific abuse conditions, swelling and strain-rate measurements have provided thermal-runaway warning lead times ranging from hundreds to more than 1,000 s. Strain, optical-fiber, and ultrasonic sensing are also examined as benchmark technologies to highlight their complementary capabilities and limitations. Building on these findings, we outline a conceptual timescale-separated mechanics-residual framework that coordinates independent normal-force prediction, force-assisted SOC estimation, slow aging-related updates, and rapid anomaly detection. This proposed framework requires further experimental validation across cell formats, operating conditions, and mechanical constraints. Overall, force and pressure sensing offer promising complementary information for improving battery-state observability and early safety warning in next-generation BMSs.]]></description>
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