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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>2026-07-08T19:47:06.44+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1835857</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1835857</link>
        <title><![CDATA[System-level lightweight design of lower-limb exoskeletons: challenges and co-design strategies]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Liancheng Zheng</author><author>Rizauddin Ramli</author><author>Shengkui Yuan</author><author>Mohamad Hazwan Mohd Ghazali</author>
        <description><![CDATA[Wearable lower-limb exoskeletons have emerged as a promising solution for rehabilitation, mobility assistance, and human performance augmentation. However, their practical deployment is limited by lightweight design challenges, particularly the trade-off between structural stiffness and system mass, the increased inertial burden caused by distal mass distribution, and the difficulty of preserving torque transmission under wearable constraints. This mini-review summarizes recent advances in lightweight design from five perspectives: structural architecture, quantitative system mass, dynamic and human-exoskeleton coupled modeling, material selection, and actuation or transmission systems. Approaches such as topology optimization, hybrid architectures, high strength-to-weight materials, remote actuation, Bowden cable transmission, and high torque-density actuation are discussed in relation to these challenges. Overall, lightweight design is identified as a system-level co-design problem requiring coordinated optimization across mechanical structure, actuation, control, and user biomechanics. Future developments are expected to focus on integrated modeling, mechanism-level synthesis, and data-driven methods to improve performance and user adaptability in next-generation exoskeleton systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1805479</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1805479</link>
        <title><![CDATA[Multi-sensor fusion control technology and its automation implementation methods for electromechanical servo systems with nonlinear friction]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaojin Lu</author>
        <description><![CDATA[IntroductionComplex mechanical systems are affected by multiphysics coupling, nonlinear friction, and time-varying disturbances, making high-precision automated operation difficult. Directly mapping heterogeneous sensing data to real-time control actions remains challenging.MethodsThis study developed a multi-sensor fusion and adaptive control framework (AFCF) by integrating a Dual-Stream Attention Mechanism (DS-AM) with an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) controller. DS-AM decouples high-frequency vibration and low-frequency current features, while prioritized experience replay and dynamic constraints improve learning efficiency and torque-execution safety.ResultsAt 5 dB noise, AFCF achieved a tracking RMSE of 0.035 mm. For a variable-curvature butterfly trajectory, it limited the maximum contour error to 4.8 μm and estimated surface roughness to 0.52 μm. Under intermittent impact, it reduced energy consumption by 9.62% and peak mechanical acceleration by 45.6%.DiscussionAFCF integrates heterogeneous perception and adaptive control in a closed loop, supporting robust and energy-aware servo control under complex conditions. Further validation with physical hardware and lower-complexity models is required for broader deployment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1859799</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1859799</link>
        <title><![CDATA[Physics-informed deep learning framework for vibration-based cylinder pressure reconstruction and heat release rate prediction in diesel engines]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Zhang</author><author>Yinhui Yu</author><author>Sumin Wu</author>
        <description><![CDATA[To address the cost and reliability issues of cylinder pressure sensors in engine digital twins, this study introduces a vibration-based, physics-informed deep learning method for reconstructing and predicting cylinder pressure and heat release rate (HRR). Coherence analysis confirmed a strong correlation (>0.8) within the 3,000–8,000 Hz band. We developed PhysFormer, a physics-constrained Transformer model that integrates 1D convolution and attention mechanisms, achieving accurate pressure reconstruction (RMSE = 0.035 bar). For forecasting, PhysFormer-Predict was designed using sliding windows and crankshaft encoding, enabling precise future pressure prediction (RMSE = 0.96 bar). To bypass error accumulation from traditional pressure differentiation, an end-to-end model (PhysHRRFormer-Predict) was built with dynamic frequency attention, directly predicting HRR from vibrations and reducing RMSE by 57% versus stepwise methods. This work provides a cost-effective, reliable single-sensor solution, significantly advancing real-time combustion monitoring for digital twins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1838542</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1838542</link>
        <title><![CDATA[Intelligent fault diagnosis method for rolling bearings based on adaptive feature mode decomposition and TCN-BiGRU-Attention]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mingli Li</author><author>Zhu Yuan</author>
        <description><![CDATA[IntroductionIn complex working environments and noisy conditions, the vibration signals of bearings are highly non-stationary, and the fault characteristics are easily masked by noise, making it difficult to effectively identify the faults. Moreover, existing methods are relatively sensitive to parameter and working condition changes and lack diagnostic stability.MethodsTo address this issue, a smart fault diagnosis method based on the Time Convolution Network - Bidirectional Gated Recurrent Unit - Attention Model (TCN-BiGRU-Attention) was proposed. This method uses the Newton-Raphson optimization algorithm to optimize the parameters of the feature mode decomposition, extracts the feature modes, and combines them with the TCN-BiGRU-Attention deep temporal sequence model to achieve multi-scale feature modes and key temporal discrimination.ResultsThe experiments were conducted based on two public datasets - Case Western Reserve University and XJTU-SY, with each group of experiments repeated at least 10 times under the same initial conditions. At the same time, ablation experiments and performance comparison experiments with other advanced methods were carried out. The results show that in the fault identification task, the accuracy of this research method reached 96.32%, which is higher than 89.15% of one-dimensional convolutional neural networks, 91.45% of bidirectional long short-term memory neural networks, and 92.84% of time convolutional networks.DiscussionIn conclusion, this method can achieve high-precision and stable fault diagnosis for rolling bearings, providing an effective intelligent diagnosis solution for engineering applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1889702</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1889702</link>
        <title><![CDATA[A comprehensive review on structural design of multi-in-one electric drive systems for new energy vehicles]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Yingshuai Liu</author><author>Xintong Li</author><author>Xiaobo Huang</author><author>Shuanglong Xu</author><author>Jianwei Tan</author>
        <description><![CDATA[The structural design of multi-in-one electric drive systems represents one of the most transformative advancements in new energy vehicle (NEV) powertrain engineering, driving the industry from conventional three-in-one configurations toward highly integrated twelve-in-one architectures. This review provides a comprehensive and systematic examination of the structural design evolution, component-level innovations, system-level integration challenges, and future trajectories of multi-in-one electric drive systems. First, the historical progression from three-in-one to twelve-in-one architectures is traced, highlighting the market-driven demand for higher power density (>3.5 kW/kg), greater efficiency (>92%), and reduced volume. Second, component-level structural design is analyzed in depth, covering permanent magnet synchronous motor (PMSM) hair-pin winding technologies, silicon carbide (SiC)-based inverter topologies, and multi-stage reducer configurations. Third, system-level integration challenges—including dual-circuit thermal management, electromagnetic compatibility (EMC) under high-frequency switching, and structural reliability validated through CAE-based modal and random vibration analysis—are critically discussed. Fourth, future trends toward domain-controller fusion, intelligent voltage boosting, and bifurcated ecosystem strategies between original equipment manufacturers (OEMs) and Tier 1 suppliers are explored. The review employs a systematic search across Web of Science, IEEE Xplore, and ScienceDirect databases (2012–2025), using keyword-based retrieval with inclusion criteria requiring relevance to structural design, system-level integration, and quantitative performance metrics, to synthesize findings from 30 peer-reviewed journal articles, offering a holistic perspective on how electromagnetic, thermal, mechanical, and control domain synergies define the next-generation of NEV powertrains. This work serves as a reference for researchers and engineers engaged in the structural optimization of highly integrated electric drive systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1875839</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1875839</link>
        <title><![CDATA[Multi-objective optimization and machine learning-based performance analysis of nano-enhanced mahua biodiesel for hybrid engines]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sinnappadass Muniyappan</author><author>I. Syed Sadiq Nawaz</author><author>Ravi Krishnaiah</author>
        <description><![CDATA[This study addresses the inadequacy of quantitative data in hybrid drivetrain applications by evaluating the performance-emission trade-offs of a compression ignition engine fueled by a novel ternary blend. The tested fuel consists of mahua biodiesel, conventional diesel, and diethyl ether (DEE) acting as a cetane improver, further enhanced with combustion-boosting nanoparticles. To determine the optimal configuration for sustainable hybrid drivetrains, a unique multi-objective optimization framework was established. Controlled engine emissions were heavily prioritized with a significance weightage of 5, while engine performance factors were assigned a weightage of 3. The experimental matrix was systematically analyzed using a comparative validation between Response Surface Methodology (RSM) and Artificial Neural Network (ANN) predictive models to isolate the absolute optimum engine load and nanoparticle concentration. The predictive models demonstrated high accuracy, with the (ANN/RSM) model yielding a superior correlation coefficient (R2) of (0.98/0.99). Optimization results identified the absolute ideal operating condition at an engine load of 77.18%, a fuel blend combination of M45D15 with 50 mg/L Al2O3 nanoparticle concentration. Under these optimal conditions, brake thermal efficiency improved by 1.6% and 1.3%, while harmful tailpipe emissions including CO, HC, smoke and NOx were reduced by 23.3% and 3.5%, 25.3% and 1.5%, 29.4% and 5.1% and 13.8% and 11.1%, respectively compared to neat diesel and M45D15 blend. These findings replace broad qualitative assumptions with precise numerical data, offering an actionable strategy for maximizing combustion characteristics and minimizing carbon footprints in next-generation hybrid engine configurations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1854200</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1854200</link>
        <title><![CDATA[Effect of Nb on the microstructure, mechanical properties and oxidation behavior of Ti-46Al-xNb alloys]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shiqiu Liu</author><author>Xuesong Xu</author><author>Hongsheng Ding</author><author>Shenghang Xu</author><author>Xing Li</author><author>Changqing Hong</author><author>R. V. Ramanujan</author>
        <description><![CDATA[The effect of Nb content on the microstructure, mechanical properties and oxidation behavior of Ti-46Al-xNb alloys was studied by thermodynamic phase diagram calculation and experiments. Our results showed that with the increase of Nb content from 0 to 9 at%, the degree of segregation of Ti-46Al-xNb alloy became larger and the dendritic morphology was more prominent. The compressive strength first increased and then decreased with Nb content increasing. The maximum value of compressive strength of 2,517 MPa was observed for the Ti-46Al-7Nb alloy, the corresponding fracture strain was 35.4%. The isothermal oxidation results at 800 °C/100 h showed that the weight gain of oxidation significantly decreased for greater Nb content. For Nb content exceeding 5 at%, the mass gain is below 0.44 mg/cm2, which is much lower than that of the TNM-B1 and Ti4822 alloys. The oxidation mechanism of TiAl-xNb alloys is further elucidated, demonstrating that the addition of Nb reduces the size and increases the density of TiO2 oxide particles, thus greatly improving the oxidation resistance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1853097</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1853097</link>
        <title><![CDATA[Nonlinear dynamic analysis of friction-induced vibrations in disc brakes]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zheng Liancheng</author><author>Chen Liguo</author><author>Yuan Shengkui</author><author>Tian Zhen</author><author>Li Feng</author>
        <description><![CDATA[IntroductionTo address the complex nonlinear dynamics and noise issues caused by frictional excitation during the braking process of disc brakes, this paper conducts systematic modeling, simulation, and structural optimization studies.MethodsFirst, a six-degree-of-freedom nonlinear dynamic model considering velocity-dependent characteristics was established, utilizing the Stribeck friction model to describe interface evolution. Subsequently, a thermo-structural coupled finite element analysis was performed in Abaqus to reveal the influence laws of braking pressure and speed on the temperature and stress fields. Finally, a topology optimization method was introduced to iteratively design the brake disc structure with the objective of minimizing strain energy.ResultsDynamic simulations revealed the bifurcation and stability characteristics of the system under different operating conditions. Finite element results indicated that braking pressure is the key factor inducing random vibrations, and verified the excellent thermal stability of carbon fiber composites. The optimized structure, while maintaining mechanical strength, significantly reduced the maximum temperature under 6000 N pressure from 132.7 °C to 118 °C, and achieved a 10% reduction in volume.DiscussionThe results demonstrate that braking pressure is the dominant factor governing system stability by modulating contact nonlinearity and negative damping, whereas vehicle speed primarily amplifies vibration intensity through thermoelastic instability. The topologically optimized structure effectively increases the air contact area and heat dissipation capacity. These findings provide a robust theoretical foundation for vibration suppression and structural improvement in braking systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1853481</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1853481</link>
        <title><![CDATA[LHSNet: large kernel hierarchical shrinkage network with deep adaptive denoising and modern wide-conv architecture for robust fault diagnosis]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yue Zhao</author><author>Yingli Li</author><author>Fangwei Luo</author><author>Xi Chen</author><author>Hongqiao Yan</author><author>Molin Su</author><author>Jinhai Wang</author><author>Dechen Yao</author><author>Jianwei Yang</author>
        <description><![CDATA[Traditional intelligent fault diagnosis techniques often struggle with signal distortion in environments characterized by strong multi-source noise, where fault signals are easily submerged and feature extraction becomes extremely unreliable. Furthermore, intelligent diagnosis is difficult to achieve using traditional denoising techniques that require extensive prior expert experience. This paper proposes a novel one-dimensional modern convolutional neural network architecture with soft thresholding denoising modules, tailored for robust mechanical fault detection under complex noise interference. Firstly, adaptive noise reduction modules are developed to assign dynamic filtering parameters to individual vibration signals, which effectively eliminates multi-source noise interference and prevents fault signal distortion. Secondly, an adaptive feature normalization mechanism is integrated to enhance signal discrimination capability while maintaining computational efficiency. Lastly, comparative experiments and ablation studies are conducted under an extended signal-to-noise ratio and diverse noise type backgrounds including Gaussian, pink, and salt-and-pepper noise to verify the superiority of the proposed architecture over existing diagnostic methods. Results have well demonstrated that the proposed approach is able to reliably identify different fault types with over 98.00% median accuracy and 96.70% peak accuracy on standard benchmark datasets. Moreover, the proposed technique maintains high diagnostic performance across diverse operating conditions with less dependency on prior denoising expertise, which shows it is well suitable for practical industrial applications with complex noise interference.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1771292</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1771292</link>
        <title><![CDATA[Identification of mechanical rotor axis trajectory state based on photogrammetry technology]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qiang Han</author><author>Xuncha Zhao</author><author>Qiuxia Lu</author><author>Xibao Wang</author><author>Xizhao Li</author>
        <description><![CDATA[IntroductionState monitoring of rotating machinery is crucial for ensuring industrial production safety, and the rotor axis trajectory is a key indicator reflecting its operating status. Traditional contact measurement methods are susceptible to factors such as electromagnetic interference, which poses a risk of data distortion.MethodsA research proposes a mechanical rotor axis trajectory state recognition method based on photogrammetry technology. Firstly, to address the issue of redundant and unstable feature points in complex operating conditions of accelerated robust feature algorithms, local two-dimensional entropy is introduced to purify candidate points. Secondly, in response to the efficiency and global shortcomings of traditional machine learning models that rely on manual parameter tuning, a support vector machine state recognition model was constructed that integrates directional gradient histogram features and transient search optimization algorithm. The transient search optimization algorithm was used to globally adaptively optimize the classifier’s penalty factor and kernel parameters.ResultsThe improved accelerated robust feature algorithm reduces the number of feature points by about 38% and reduces trajectory reconstruction error by nearly half. The transient search optimization support vector machine model constructed has an average classification accuracy improvement of about 3% and a prediction speed improvement of nearly three times compared to other comparative models.DiscussionThe proposed solution successfully constructs a complete link from high-precision visual trajectory acquisition to state recognition, which not only provides a new non-contact solution for traditional contact measurement problems, but also has important engineering application value for improving the predictive maintenance level of equipment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1843693</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1843693</link>
        <title><![CDATA[Multi-task collaborative recognition technology for intelligent driving vehicles driven by computer vision]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huandong Wang</author><author>Jiayu Yang</author><author>Ruomiao Wang</author><author>Haozuo Su</author><author>Xuanzheng Wang</author>
        <description><![CDATA[IntroductionTo develop a multi-task collaborative intelligent driving perception system enabling high-precision integrated recognition of pedestrians, roads, and vehicles, this study proposes a corresponding recognition technology.MethodsFor pedestrian detection: multi-scale dynamic binning coding and cross-modal attention fusion architecture. For road segmentation: lightweight network with hybrid attention convolution. For vehicle perception: 3D recognition system based on voxel feature fusion. Multi-task collaboration is achieved via dynamic task priority scheduling and multi-source result fusion calibration.ResultsExperiments show: pedestrian average accuracy 96.78%, average per-frame inference latency 14.82 ± 0.67 ms (80,000-frame dataset); road segmentation IoU 92.34%; 50 m vehicle positioning error 0.23 m; multi-target comprehensive accuracy 89.76%; complex-scene false detection rate 1.23%, missed detection rate 0.89%; 8-h continuous operation performance degradation rate 0.56%.DiscussionThis study enhances the accuracy and stability of intelligent driving perception in complex scenes, provides reliable perception support for autonomous driving decision-making systems, and has significant practical value for the application of multi-task collaborative sensing technology in intelligent driving.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1869741</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1869741</link>
        <title><![CDATA[Fatigue life prediction of flexible spacecraft solar arrays under multi-source loading via a physics-informed dual-stream neural network]]></title>
        <pubdate>2026-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Minghang Li</author><author>Hanwen Pu</author><author>Xingzhi Yang</author><author>Geng Chen</author>
        <description><![CDATA[IntroductionFlexible spacecraft solar arrays are subjected to multi-source thermo-vibro-mechanical loading in orbit. High-fidelity finite element analysis (FEA) is computationally prohibitive in high-cycle fatigue workflows. Purely data-driven surrogates are prone to overfitting in limited-data regimes and lack constraints rooted in basic mechanical principles. To address these challenges, this paper proposes Phy-Dual-Net, a physics-informed dual-stream neural network for multi-source fatigue life prediction.MethodsA dataset of 450 loading cases is constructed using FEA. Rainflow counting is applied to each load sequence to extract dominant stress cycles, to which Goodman mean stress correction is then applied to compute a physics-informed feature Sphy, embedding a physically grounded damage reference into the network input. An asymmetric dual-stream architecture is adopted: one stream captures cyclic loading effects from a temporal perspective, while the other aggregates macroscopic statistics to constrain fatigue damage accumulation. A hybrid loss function combining data-driven regression with physics-based regularization and a monotonicity penalty guides the network to obey fundamental fatigue damage laws.ResultsOn the testing set (N=135), Phy-Dual-Net, enhanced by test-time augmentation and deep ensemble inference, achieves R2=0.9966 with every prediction confined within the factor-of-two scatter band. Across five independent training runs, the model yields R2=0.9904±0.0010 with stable convergence.DiscussionCompared with the computational cost of high-fidelity FEA, Phy-Dual-Net reduces the expense of fatigue life evaluation while preserving physically consistent responses across the full fatigue life range.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1873624</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1873624</link>
        <title><![CDATA[Flow organization and hydraulic-loss-aware design of additively manufactured heat exchangers]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Rongji Tang</author><author>Meixue Song</author><author>Wei Ji</author><author>Yue Zhao</author><author>Jinpan Chang</author><author>Liansong Wang</author><author>Liancheng Zheng</author><author>Xianzheng Liu</author>
        <description><![CDATA[Additive manufacturing has changed the way compact heat exchangers are designed, not only because it enables complex internal geometries, but also because it allows flow passages to be treated as functional fields rather than simple ducts. In conventional heat exchanger design, heat transfer enhancement is often obtained by increasing surface area, adding fins, or disturbing the boundary layer. These approaches remain effective, but they also introduce flow maldistribution, local recirculation, and pressure-drop penalties. This mini-review discusses additively manufactured heat exchangers from a flow-physics perspective, with particular attention to the relationship between geometry-induced flow structures, hydraulic loss, and convective heat transfer. Instead of reviewing additive manufacturing methods or materials in a broad manner, the discussion focuses on microchannels, manifold channels, biomimetic networks, lattice-based architectures, triply periodic minimal surface structures, and topology-optimized flow paths. The review highlights that the central challenge is no longer simply to maximize heat transfer area, but to organize vortices, boundary-layer renewal, flow splitting, and fluid–solid contact in a controlled way. Current numerical and machine-learning approaches are also discussed as tools for linking local flow behavior with global thermal–hydraulic performance. Finally, future directions are proposed for developing heat exchangers that are not only compact and efficient, but also hydraulically balanced, manufacturable, and application-specific.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1827441</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1827441</link>
        <title><![CDATA[Adaptive gait planning and performance evaluation of a lightweight three-bar tensegrity robot]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liancheng Zheng</author><author>Rizauddin Ramli</author><author>Ani Luo</author><author>Heming Zhao</author><author>Heping Liu</author><author>Guosong Chen</author>
        <description><![CDATA[This study presents a dynamic modeling and gait optimization framework for a mobile 3-bar tensegrity robot aimed at enhancing locomotion efficiency and stability on complex terrains. A complete Lagrangian dynamic model incorporating ground contact dynamics is established to accurately describe the nonlinear behavior of the tensegrity structure. Based on this model, a periodic actuation strategy is developed, and gait parameters for both straight-line and turning motions are optimized using the NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective algorithm. The optimization simultaneously maximizes displacement and minimizes lateral deviation or centroid offset. A quantitative performance evaluation system which comprise of locomotion rate, specific energy consumption, and center-of-mass fluctuation is proposed to assess gait effectiveness. Simulation results demonstrate that increased active cable velocity improves motion efficiency and energy economy without compromising stability. The proposed method achieves a maximum straight displacement of 52 mm, a clockwise turning angle of 23°, and a counterclockwise turning angle of 13°. These results confirm that the proposed NSGA-II–based optimization framework effectively enhances the efficiency, stability, and adaptability of gait planning and control for lightweight tensegrity robots in unstructured environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1863228</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1863228</link>
        <title><![CDATA[Two-stage optimization design of cylindrical lattice structures for low-frequency vibration attenuation]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yonghao Zhang</author><author>Shaowei Song</author><author>Mao Yang</author><author>Chang Liu</author><author>Zhaolin Chen</author>
        <description><![CDATA[IntroductionTo address the mass-law limitation inherent in conventional passive vibration isolators, this study introduces a novel micro-scale hollow-scatterer cylindrical lattice architecture and develops a two-stage optimization framework for achieving broadband low-frequency vibration attenuation.MethodsIn the first stage, a locally resonant unit cell is engineered, comprising a lead-based hollow scatterer, a viscoelastic rubber coating, and a thermosetting resin connector. A Bloch-theory-informed two-degree-of-freedom (2-DOF) reduced-order model is formulated to provide a mechanistic and computationally efficient prediction of the first locally resonant bandgap. Leveraging parametric sensitivity analysis and the Box-Behnken response surface methodology, seven critical geometric parameters are systematically optimized. In the second stage, curvature is deliberately introduced as an additional design degree of freedom by transforming the planar connector into a single-curvature cylindrical configuration. Further parametric investigation and experimental validation are conducted to characterize the attenuation performance.ResultsThe resulting optimized unit cell achieves a 32.7% reduction in the lower bandgap cutoff frequency (to 595.2 Hz) and a 64.9% expansion of the absolute bandgap width (to 762.8 Hz) with model prediction errors consistently below 10%. The curvature-induced geometric stiffening effect shifts the upper bandgap boundary mode from pure out-of-plane bending to a hybrid bending-membrane coupling mode, thereby broadening the bandgap to 1,418.16 Hz. Experimental validation using vibration testing shows reasonable agreement with numerical predictions in terms of attenuation onset and relative bandwidth trend.DiscussionCollectively, this work establishes a synergistic design paradigm integrating data-driven parametric modeling with curvature-mediated structural tailoring, providing a robust, scalable methodology for the rational design of lightweight, high-performance low-frequency vibration isolation systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1879342</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1879342</link>
        <title><![CDATA[MXene-integrated damping and vibro-acoustic composites: from nanosheet interfaces to structural dynamics]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Haotian Wu</author><author>Peikai Zhang</author><author>Shengjun Ji</author><author>Bangsheng Yin</author><author>Wenyue Si</author>
        <description><![CDATA[Vibration and noise control are critical for lightweight engineering structures, yet conventional damping and sound-absorbing materials often suffer from limited low-frequency performance, mechanical–damping trade-offs, insufficient multifunctionality, and a lack of real-time feedback. MXenes, a family of two-dimensional transition-metal carbides, nitrides, and carbonitrides, possess layered nanosheet structures, high electrical conductivity, abundant surface terminations, and good processability, providing a promising platform for multifunctional vibro-acoustic composites. Unlike previous MXene reviews that mainly focus on sensing, electromagnetic shielding, thermal management, or mechanical reinforcement, this mini review establishes a mechanism-oriented framework linking nanosheet interfacial dissipation, structural attenuation, and self-sensing response under dynamic loading. Recent progress in MXene-integrated damping and vibro-acoustic composites is summarized, including MXene/polymer interfacial dissipation, porous and layered structures for sound absorption and vibration attenuation, and conductive networks for self-sensing damping and structural health monitoring. Key challenges, such as inconsistent evaluation criteria, unclear dissipation mechanisms, limited long-term stability, and insufficient coupling between material design and structural dynamics modeling, are further discussed. Future interface–structure–function integrated design may enable MXenes to evolve into intelligent vibro-acoustic control units with energy dissipation, state sensing, and multifunctional coupling capabilities.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1872059</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1872059</link>
        <title><![CDATA[Simulation research on temperature rise laws of pantograph sliders based on thermo-electro-mechanical multi-field coupling]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chang Wei</author><author>Lin Weng</author><author>Lin Chen</author><author>Zixuan He</author>
        <description><![CDATA[IntroductionWith the continuous increase in operating speeds and current-carrying capacities of urban rail transit systems, thermal failure of pantograph contact strips during current collection has become an increasingly prominent issue. This study investigates the influence of operational parameters on the temperature field distribution and evolution process of the contact strip, thereby providing data references for its optimal design under working conditions.MethodsTaking the QG-120(B) type single-arm pantograph commonly used in metro Type B vehicles as the research object, a transient temperature rise simulation model of the pantograph–catenary system was constructed using ABAQUS finite element software based on thermo-electro-mechanical coupling theory. A three-factor, four-level orthogonal experiment was designed by considering traction current, normal load, and operating speed. Range analysis was then introduced to quantitatively evaluate the influence weight of each factor on the maximum temperature of the contact strip.ResultsThe simulation results indicate that, within the selected range of operating conditions, the maximum temperature of the contact strip exhibits a significant nonlinear relationship with each influencing factor. By calculating the mean values and corresponding ranges of each factor at different levels, traction current was found to have the greatest influence on temperature rise, followed by normal load and sliding speed.DiscussionThis study preliminarily reveals the variation trends of contact strip temperature rise under different operating parameters and identifies the optimal operating condition for the lowest average temperature within the specified parameter range: a normal load of 20 N, a traction current of 200 A, and an operating speed of 80 km/h. The results provide useful data support for the thermal response analysis and operating condition optimization of pantograph contact strips.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1837376</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1837376</link>
        <title><![CDATA[Steering technology of autonomous vehicle based on C-EPS and fuzzy control theory]]></title>
        <pubdate>2026-06-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaohua Li</author>
        <description><![CDATA[BackgroundWith the rise of industrial modernization, autonomous vehicle steering technology is becoming increasingly vital for safety and precision control. However, the Electric Power Steering (EPS) system commonly used in autonomous driving faces challenges such as insufficient steering angle accuracy and large response delays.MethodsThis study proposes a system model based on a column EPS system and designs a controller integrating the fuzzy control theory with a proportional-integral-derivative method.ResultsPerformance evaluation shows that the proposed controller achieves a stability time of 0.18 s and 0% overshoot, outperforming the comparison controller. Additionally, at 25 km/h, it maintains a centroid lateral deflection angle of 0.2°, demonstrating superior stability.DiscussionThe above quantitative results fully demonstrate that the research has solved the problems of insufficient steering angle accuracy and significant response delay. These findings highlight the controller’s practical value in optimizing autonomous vehicle steering, improving safety, and advancing steering control theory.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1861443</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1861443</link>
        <title><![CDATA[A quality-preserving model for test reduction in electronics production]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Einav Peretz-Andersson</author><author>Noufa Haneefa</author><author>Teddy Lazebnik</author>
        <description><![CDATA[IntroductionManufacturing test flows in high-volume electronics production are typically fixed during product development and executed unchanged on every unit, even as failure patterns and process conditions evolve. This protects quality, but it also imposes unnecessary test cost, while existing data-driven methods mostly optimize static test subsets and neither adapt online to changing defect distributions nor explicitly control escape risk.MethodsIn this study, we present an adaptive test-selection framework that combines offline minimum-cost diagnostic subset construction using greedy set cover with an online Thompson-sampling multi-armed bandit that switches between full and reduced test plans using a rolling process-stability signal. We evaluate the framework on two printed circuit board assembly stages—Functional Circuit Test and End-of-Line test—covering 28,000 board runs.ResultsOffline analysis identified zero-escape reduced plans that cut test time by 18.78% in Functional Circuit Test and 91.57% in End-of-Line testing. Under temporal validation with real concept drift, static reduction produced 110 escaped defects in Functional Circuit Test and 8 in End-of-Line, whereas the adaptive policy reduced escapes to zero by reverting to fuller coverage when instability emerged in practice.DiscussionThese results show that online learning can preserve manufacturing quality while reducing test burden, offering a practical route to adaptive test planning across production domains, and offering both economic and logistics improvement for companies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1844316</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1844316</link>
        <title><![CDATA[Numerical investigation of cavitation performance and critical sigma characterization in a 55 kW propeller turbine]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Thaithat Sudsuansee</author><author>Noppong Sritrakul</author><author>Yodchai Tiaple</author>
        <description><![CDATA[IntroductionThis study presents a computational fluid dynamics (CFD) investigation of cavitation performance in a 55 kW horizontal-axis propeller turbine using ANSYS CFX.MethodsA total of 43 steady-state RANS simulations were performed employing the SST k–ω turbulence model and the Zwart–Gerber–Belamri (ZGB) cavitation model across five net heads (H = 3, 5, 7, 9, and 11 m) with cavitation numbers from σ = 0.20 to 1.50 at N = 700 rpm. The critical cavitation number (σc) was determined using the 1% efficiency drop criterion per IEC 60193.ResultsThe best efficiency point under non-cavitating conditions was identified at H = 7 m and σ = 0.80, yielding a hydraulic efficiency of 81.36% and a shaft power of 45.51 kW (with a maximum overall efficiency of 81.79% observed at σ = 0.80 under moderate cavitation). The critical cavitation number was found to decrease systematically with increasing head, from σc ≥ 1.50 at H = 3 m (where persistent cavitation occurred due to rotational speed mismatch) to σc = 0.60 at H = 11 m. The reported σc values are upper-bound estimates constrained by the discrete sigma-sweep resolution. An efficiency hill chart and a cavitation risk map were generated in the H–σ plane, providing a practical operational guide. Two noteworthy computational phenomena were observed: an apparent efficiency enhancement at H = 11 m under moderate cavitation, and an increase in the computed mass flow rate under severe cavitation.DiscussionThese phenomena may be influenced by numerical artifacts and require transient verification. The results indicate that the recommended operating range for this turbine at N = 700 rpm is H = 7–9 m with σ > 0.80, ensuring efficiency above 79% with minimal cavitation risk.]]></description>
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