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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>2026-05-02T02:38:56.840+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1818168</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1818168</link>
        <title><![CDATA[The preparation and properties of fibers based on V-HDPE/R-HDPE mixtures with different mass ratios]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoguang Tian</author><author>Xiaoyang Li</author><author>Jiameng Yang</author><author>Syazwie Adam Sapihi</author><author>Gan Jet Hong Melvin</author><author>Bih Lii Chua</author>
        <description><![CDATA[In order to explore how different mass ratio combinations of virgin high-density polyethylene (V-HDPE)/recycled high-density polyethylene (R-HDPE) affect fibre and print quality, this study conducts a series of analyses. Fused deposition modelling (FDM) is a process used for fibre preparation investigation; it is possible to include five groups of ratios between R-HDPE 100% and V-HDPE 100%. The process of filament extrusion is supplemented with optical microscope observation. This helps establish a correlation between the composition and microstructure of any bio-based or crude oil-derived material; thus, it achieves a bio-based or crude oil-derived derivative. The diameter uniformity is tested; tensile strength is evaluated; printing parameters are optimized. The tensile strength of the filament reaches the maximum value (25.7 ± 1.1 MPa) when the mass ratio of V-HDPE40%/R-HDPE60%; it is higher than 38.2% that of pure R-HDPE (18.6 ± 0.8 MPa). The tensile strength of the V-HDPE60%/R-HDPE40% system stands at 24.3 ± 1.0 MPa; it rises by 30.7% against pure R-HDPE and still outperforms pure V-HDPE and low-proportion mixed systems. The average fibre diameter remains consistently within the range of 1.86–1.90 millimeters (mm) when the V-HDPE content is between 20% and 40%. Controlling the diameter to within ±0.05 mm improves the layers’ adhesion. In contrast, the V-HDPE100% group has a distinct diameter with weak interlayer adhesion. The results show that the strength of the composites increases with the rise of V-HDPE content; it reaches the maximum at 40% V-HDPE. This means that the properties of 3D printing fibers manufactured from recycled materials can be improved by controlling the mixing and printing parameters; this makes it easy to develop sustainable composite materials for aerospace and automobile manufacturing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1784016</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1784016</link>
        <title><![CDATA[Ignition and burning behavior of live foliage: single elements vs. groups]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nathan Gardner</author><author>David Blunck</author>
        <description><![CDATA[This study investigated the ignition and burning behavior of single foliage elements and naturally occurring foliage groups under controlled convective heating. Across all species, droplet ejection and combustion was observed, with more frequent ejection and burning occurring in foliage groups. The combustion of ejected droplets represented a distinct ignition phenomenon and contributed to early heat release, particularly in conifers. In some cases, the burning of ejected droplets occurred simultaneously with the burning of the foliage. Ignition behavior differed between single foliage elements and foliage groups. At low heat fluxes, foliage groups were more likely to ignite than single elements due to higher local equivalence ratios from greater pyrolyzate mass flux, while no sensitivity to foliage grouping was observed at higher fluxes. Foliage groups exhibited longer and more variable flaming durations than single elements, reflecting sequential ignition and larger available fuel mass. Unlike ignition time, flaming duration showed no consistent dependence on heat flux, indicating that fuel traits and arrangement exert stronger influence. These findings highlight the potential importance of incorporating fuel geometry and foliage grouping effects into fire behavior models, particularly for ignition under low heating conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1798703</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1798703</link>
        <title><![CDATA[Effect of micro-pit arrangement on the behavior of gas parallel slider bearings]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fuxi Liu</author>
        <description><![CDATA[Surface texturing is helpful in decreasing the friction, decreasing the wear, and increasing the fluid film stiffness. This investigation studies the influence of micro-pit arrangement on the behavior of gas parallel slider bearings (GPSB) by employing the multi-grid finite element method using the Matlab software. The pressure distribution of GPSB with micro-pits was investigated. The results showed that the position offset ratio of micro-pits had negligible effect on the maximum pressure and minimum pressure of GPSB with micro-pits. Meanwhile, the effect of the geometrical parameters of micro-pits on the average pressure of GPSB was investigated for different position offset ratios. Geometrical parameters of micro-pits included depth of micro-pits, radius of micro-pits, transversal spacing, longitudinal boundary distance, and longitudinal spacing. It was found that both depth and radius of micro-pits corresponding to maximized average pressure were controlled by the position offset ratio of micro-pits. The findings of this paper imply that the optimized micro-pit arrangement could improve the hydrodynamic pressure of gas parallel slider bearings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1781909</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1781909</link>
        <title><![CDATA[Defect-controlled fatigue mechanisms of LPBF 316L stainless steel: a mini-review]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Rongji Tang</author><author>Zainuddin Sajuri</author><author>Anfu Guo</author><author>Xianzheng Liu</author><author>Yuedong Zou</author><author>Shaoqing Wang</author><author>Feng Li</author>
        <description><![CDATA[Laser powder bed fusion (LPBF) enables rapid fabrication of 316L stainless steel (SS 316L) with complex structures, but its rapid solidification and layer-by-layer deposition inevitably induce defects, such as pores and lack of fusion (LOF), severely restricting fatigue performance. This mechanics-focused mini-review summarizes recent research on the fatigue performance of SS 316L, focusing on the interaction mechanism between defect characteristics and the principal normal stress. The effects of build orientation and processing parameters on defect features are discussed, and the mechanisms by which defects influence fatigue under low-cycle fatigue (LCF), high-cycle fatigue (HCF), and various loading modes (tension, bending, and torsion) are compared. Furthermore, this review discusses the mechanisms and limitations of different post-processing methods, including heat treatment (HT), hot isostatic pressing (HIP), and surface treatments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1779287</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1779287</link>
        <title><![CDATA[Experimental and theoretical evaluation of a humidification dehumidification desalination using vortex tube]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hani Abdulelah Abulkhair</author>
        <description><![CDATA[This study presents the experimental and theoretical evaluation of a HDH desalination system integrating vortex tube and bubble column. The motivation stems from the need for energy-efficient and scalable solutions to address freshwater scarcity, particularly in regions with limited access to conventional desalination infrastructure. We design a system comprising an air compressor, vortex tube, bubble column, and double-tube condenser, where pressurized air is separated by vortex tube into hot and cold streams where both streams used to facilitate vapor generation and condensation. Experimental tests are conducted at heater temperatures of 60 °C, 70 °C, and 80 °C, revealing that water production peaks at 70 °C with a rate of 0.098 g/s, whereas higher temperatures exhibit diminishing returns. Theoretical analysis confirms the system’s thermal efficiency, with an overall heat transfer coefficient of 2.98 W/m2·K and a condenser length of 1.17 m is required to achieve a heat transfer rate of 17 W. The logarithmic mean temperature difference (LMTD) is calculated as 28.8 K, validating the design’s effectiveness. The results demonstrate a strong alignment between experimental and theoretical predictions, underscoring the system’s feasibility for practical deployment. The novelty of this work lies in the synergistic integration of vortex tube and bubble column technologies, which has not been addressed n the previous studies. This research contributes to advancing sustainable desalination methods, offering a promising alternative to traditional energy-intensive processes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1815704</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1815704</link>
        <title><![CDATA[Novel beam analogy model for force distribution analysis in abrasive water jet machining]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Marta Harničárová</author><author>Leonard Dütsch</author><author>Jan Valíček</author><author>Milan Kadnár</author><author>Hakan Tozan</author><author>Milena Kušnerová</author>
        <description><![CDATA[BackgroundAbrasive water jet (AWJ) machining is a common technology in modern manufacturing due to its ability to cut any material without causing heat damage. However, many existing models require material-dependent calibration constants, making them impractical.ObjectiveThe current study proposes a beam analogy model that treats microscopic surface details as cantilever beams subjected to a constant load of the abrasive jet.MethodsBased on Bernoulli-Navier beam theory, we propose a general material parameter Kawj=1012/Emat2 to link material cuttability with Young’s modulus. The experiment conducted 750 trials over 10 different materials (Emat = 45–210 GPa) and then combined the results with Physics-Informed Neural Network (PINN), along with sensitivity and dimensional analyses. The outcome revealed the model to be in strong agreement with the experimental results, with R2 values of 0.94 for surface roughness and 0.91 for jet lag. Adding the PINN component improved the model’s predictive ability to R2 = 0.97, yet it remained physically valid when extrapolating. Sensitivity analysis revealed a material-independent relative sensitivity of −2, meaning that a 1% uncertainty in Young’s modulus corresponds to a 2% uncertainty in Kawj. The universal scaling law unified the results for various materials, while the depth-dependent Kawj model explained the evolution of roughness with cutting depth. We further introduced dimensionless parameters ΠAWJ, Γstab, and ηAWJ to describe process similarity, stability, and energy efficiency, respectively.ConclusionThe method provides a physics-informed framework for AWJ process modeling, which can be used for accurate prediction and optimization of the process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1790709</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1790709</link>
        <title><![CDATA[Microstructure and property evolution of pyrolysed epoxy-derived CFRP/SiC composites for non-structural thermal protection applications]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mahfud Ibadi</author><author>Sugeng Supriadi</author><author>Isa Anshori</author><author>Rikson Asman Fertiles Siburian</author><author>Yatimah Alias</author><author>Yudan Whulanza</author>
        <description><![CDATA[Epoxy-derived carbon fiber–reinforced polymer composites are promising for lightweight high-temperature applications, yet their performance after thermal conversion strongly depends on processing conditions. This study investigates the effects of pyrolysis atmosphere (vacuum and argon) and SiC particle addition on the microstructure–property relationships of epoxy-based CFRP and CFRP/SiC laminates pyrolyzed at 800 °C. Laminates were fabricated by hand lay-up and vacuum-assisted impregnation, followed by staged pyrolysis. The materials were characterized by density measurement, Shore D hardness, three-point bending, scanning electron microscopy (SEM), and Raman spectroscopy. The results show that the pyrolysis atmosphere plays a dominant role in controlling porosity development and matrix continuity. Vacuum treatment promotes aggressive volatile release, leading to lower density, more severe interlaminar porosity, and greater mechanical degradation than argon. Argon processing enables higher char retention and improved microstructural coherence. SiC addition significantly increases hardness in both green-body and post-pyrolysis states due to its intrinsic rigidity; however, flexural strength in both systems drastically decreases (>93%) after pyrolysis, indicating that bending performance is governed by matrix continuity rather than local hardness. SEM confirms extensive fiber exposure and porous carbonaceous residues, while Raman verifies successful polymer-to-carbon conversion. These findings suggest that epoxy-derived CFRP/SiC systems are more suitable for non-load-bearing thermal protection system (TPS) applications, such as insulating or ablative layers, than structural components.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1802237</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1802237</link>
        <title><![CDATA[Advances in multi-material polymer 3D printing depositions: techniques, materials combinations, challenges, and emerging applications]]></title>
        <pubdate>2026-04-21T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Asad A. Zaidi</author><author>Muhammad Muzamil</author><author>Muhammad Asif Ali</author><author>Muhammad Asif</author><author>Rashid Ali Laghari</author><author>Jin Peng</author><author>Sohaib Z. Khan</author>
        <description><![CDATA[Multi-material polymer additive manufacturing enables the fabrication of components with spatially varied mechanical, thermal, electrical, and functional properties within a single build, overcoming the intrinsic limitations of single-material 3D printing. By combining thermoplastics, elastomers, photopolymers, and functional polymer composites, this approach allows material composition to be programmed alongside geometry, enabling monolithic fabrication of multifunctional and heterogeneous polymer systems. This review critically surveys recent advances in multi-material polymer additive manufacturing across major process families, including material extrusion, vat photopolymerization, material jetting, powder bed fusion, and emerging hybrid platforms. Key material systems and deposition strategies are examined with particular emphasis on interfacial adhesion, thermal-mechanical compatibility, and process reliability, which collectively govern the performance of multi-material printed parts. The review further synthesizes current challenges related to material integration, hardware and software complexity, and post-processing, and highlights representative application domains such as soft robotics, biomedical devices, embedded electronics, aerospace tooling, and functionally graded structures. By consolidating fabrication strategies, material considerations, and application-driven insights, this work provides a structured reference for advancing the design and implementation of multi-material polymer additive manufacturing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1797900</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1797900</link>
        <title><![CDATA[Research on adaptive optimization of unmanned aerial vehicle transmission line inspection imaging under strong and weak light conditions]]></title>
        <pubdate>2026-04-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haibo Ru</author><author>Ziqiang Lu</author><author>Jie Li</author><author>Huiwei Liu</author><author>Ziying Lu</author>
        <description><![CDATA[This study focuses on the problem of image quality degradation caused by drastic changes in light intensity during unmanned aerial vehicle inspection of power transmission lines, and proposes a three-level adaptive optimization framework of light perception, parameter adjustment, and image enhancement. This framework precisely identifies the light conditions through a multi-feature fusion model (with an accuracy rate of 97.3% for light scene classification), dynamically optimizes the core parameters of the camera based on reinforcement learning, and completes image enhancement by combining the lightweight network LW-EnhanceNet. Experiments show that the proportion of overexposure in strong light scenarios has decreased from 35.2% to 7.8%, the signal-to-noise ratio in weak light scenarios has reached 37.5 dB (an increase of 54.9% compared to the original image), and the F1-score for defect detection has reached 89.7%; in real scenarios, the rate of image quality meeting standards has increased to 91.0%, and the inspection efficiency has increased by 24.8%. This method provides an effective solution to the imaging bottleneck in all-weather inspections, and has significant theoretical and practical significance for improving the automation level of inspections and the accuracy of defect detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1781086</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1781086</link>
        <title><![CDATA[Intelligent fault diagnosis of coal mine roadheaders based on particle swarm optimization of BP neural networks]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Junling Feng</author><author>Ye Zhang</author><author>Ailiang Kang</author>
        <description><![CDATA[IntroductionCoal mine roadheaders operate under complex conditions characterized by prolonged exposure to high vibration and dust levels, resulting in a high failure rate. Traditional fault diagnosis methods suffer from issues such as low diagnostic accuracy and poor real-time performance. This study suggests an intelligent diagnosis model for coal mine roadheader faults based on the artificial fish swarm algorithm, particle swarm optimization, and a backpropagation neural network in an attempt to improve the precision and effectiveness of fault diagnosis for coal mine roadheaders and guarantee safe equipment operation.MethodsInitial data is processed through statistical methods, correlation analysis, and normalization. A backpropagation neural network is selected as the fundamental diagnostic model, with the artificial fish swarm algorithm and particle swarm optimization algorithm introduced to perform global optimization of its initial weights and thresholds. The network is trained using forward propagation and error backpropagation mechanisms, and its outputs are converted into fault probability distributions.ResultsExperimental outcomes indicated that the training loss and test loss values of the research method differed by 0.01. Its classification accuracy remained consistently above 95% across varying TS proportions. In practical application testing, the best fitness and AF of the research method both exceeded 0.8 overall. Its omission rate reached a stable value of 9.8% at an 80% load rate.DiscussionThe above results demonstrate that the research methodology exhibits high diagnostic accuracy and efficiency, effectively addressing issues such as insufficient precision and low detection efficiency in traditional approaches. This enhances the safety and reliability of intelligent fault diagnosis for coal mine roadheaders.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1757067</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1757067</link>
        <title><![CDATA[Adsorption behavior of sodium carboxylates on iron oxide surface in water and their tribological properties]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tomoaki Okano</author><author>Hideaki Hattori</author><author>Naoki Akamatsu</author><author>Naoki Yamashita</author><author>Tomoko Hirayama</author>
        <description><![CDATA[The tribological performance of sliding surfaces in water can be improved by using lubricating solutions containing additives such as water-soluble sodium carboxylates, which reduce friction and prevent corrosion. These additives adsorb onto metal surfaces to exert their functions; however, the structural characteristics at the metal-solution interface and their influence on tribological properties remain largely unexplored. This paper investigated how water-soluble sodium carboxylates adsorb on iron oxide in water and how the adsorption structure governs boundary lubrication. To investigate the effect of headgroup multiplicity at a constant chain length (C10), sodium decanoate (monocarboxylate) and disodium sebacate (dicarboxylate) were compared as sample additives. Reciprocating ball-on-disk tests showed that sodium decanoate reduced the steady state friction coefficient by ≈70% relative to pure water, whereas disodium sebacate provided only a limited reduction and friction increased with cycling. Neutron reflectometry and frequency-modulation atomic force microscopy (FM-AFM) revealed that sodium decanoate forms a structured adsorption layer with an interfacial thickness of ≈3.5 nm or more, while disodium sebacate produces a much less ordered layer. Quartz crystal microbalance with dissipation monitoring (QCM-D) further indicated faster adsorption time for sodium decanoate than for disodium sebacate. These results support the hypothesis that low friction in aqueous lubrication requires not only adsorption but also the formation of a sufficiently thick and ordered boundary layer on iron oxide.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1740194</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1740194</link>
        <title><![CDATA[Numerical investigation of macroscopic and microscopic characteristics of an air-assisted spray from a multi-hole nozzle]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lei Li</author><author>Jianlong Bu</author><author>Feixiang Chang</author><author>Qiang He</author><author>Aoshuang Ding</author><author>Lin Chen</author><author>Huiyu Sun</author>
        <description><![CDATA[To facilitate the transition of marine energy systems toward low-carbon and environmentally sustainable solutions, this study numerically investigates the medium-assisted atomization characteristics of a marine methanol tri-fuel boiler (methanol, diesel, and heavy fuel oil) under realistic operating conditions. A CFD-based VOF-DPM framework coupled with adaptive mesh refinement (AMR) is employed, in which the VOF method captures the evolution and breakup of the continuous liquid phase, while the DPM tracks the dispersed droplets and their size characteristics, with AMR applied to both phases to ensure adequate spatial resolution. Key parameters analyzed include the evolution of co-current atomization for different fuels, velocity-field distribution, droplet-size distribution, and atomization angle. The results demonstrate that methanol, owing to its low viscosity and surface tension, generates fine and uniformly distributed droplets predominantly within the 0–10 μm range, with a maximum atomization angle of 25°–27° and a Sauter mean diameter (SMD) consistently below 100 μm. Diesel produces droplets concentrated mainly between 40 and 50 μm, accompanied by noticeable fluctuations in atomization uniformity, whereas heavy fuel oil exhibits pronounced primary fragmentation after saturated-steam-assisted atomization, yet its velocity decays rapidly. Distinct differences are observed among the three fuels in terms of spray-penetration distance and atomization angle: methanol shows the strongest lateral dispersion but the weakest axial penetration, heavy fuel oil displays the opposite trend, and diesel lies between the two. This study elucidates the effects of fuel properties and atomizing media on the spray performance of marine methanol-fired triple-fuel boilers, offering theoretical insights for nozzle optimization and the practical application of low-carbon fuels in marine energy systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1734239</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1734239</link>
        <title><![CDATA[Analysis of elastic characteristics of a transverse leaf spring with high-static-low-dynamic stiffness]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhi Li</author><author>Zhouquan Li</author><author>Huawei Wu</author><author>Zhen Liu</author>
        <description><![CDATA[A variable-stiffness transverse leaf spring offers clear advantages in lightweight design, load-bearing capability, and vibration mitigation, indicating strong potential for broad application. However, the shock-vibration attenuation of conventional leaf springs is typically below 80%, which limits the ride comfort of leaf-spring suspensions under impact excitation. In this study, a high-static–low-dynamic stiffness (HSLDS) transverse leaf spring is proposed, and its elastic characteristics are investigated through numerical simulations and experimental validation. The results show that the static stiffness increases with load, reaching a maximum improvement of 35%. In contrast, the dynamic stiffness decreases with increasing excitation intensity, with a maximum reduction of 20.4%. Moreover, the vibration isolation ratio of the proposed leaf-spring suspension exceeds 90%. These findings provide a theoretical basis and practical reference for the design, analysis, and performance evaluation of transverse leaf springs with strongly nonlinear stiffness characteristics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1807233</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1807233</link>
        <title><![CDATA[Electromagnetic characteristic of a novel hybrid excitation generator for vehicles based on the field-and-circuit method]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jianwei Ma</author><author>Xianjun Zeng</author><author>Jun Liu</author><author>Kaikai Shao</author><author>Hao Huang</author><author>Xiaojia Zhang</author><author>Zengpu Xun</author>
        <description><![CDATA[IntroductionThe electro-magnetic generator commonly used in automobiles generally has high excitation loss leading to low efficiency. And it cannot effectively provide power for the vehicle, thus shortening battery life.In order to solve the issues, a parallel double-rotor hybrid excitation generator is proposed, consisting of a radial permanent magnet rotor and a claw-pole electro-magnetic rotor that share the same stator core.MethodsUsing the equivalent magnetic circuit method, the magnetic circuit models of the generator are established, and the computational formula of synthetic permeance, synthetic leakage permeance, effective flux, and leakage flux are derived. Taking advantage of the theory of a permanent magnet motor and electro-magnetic generator, the structural parameters are designed. Furthermore, a finite element model is established according to the structural parameters, the permanent magnet field is analyzed, and the parameters of the stator slots and claw poles are optimized according to the results of magnetic field analysis. Moreover, the mechanism of magnetic field synthesis and adjustment are verified. The influences of the claw-pole parameter on the output voltage waveform and air-gap flux density are analyzed, and the optimum value claw tip’s pole-arc coefficient is determined. Using the equivalent magnetic circuit and finite element methods, the magnetic flux density of the yoke of the permanent magnet rotor, the root of the paw-pole, and the flange and the excitation winding bracket are calculated.ResultsThe influence of exciting current on noload terminal voltage is further analyzed, demonstrating that the hybrid excitation generators have excellent voltage adjustment characteristics. It is verifying that the field-and-circuit method can improve the accuracy of electromagnetic analysis. Using the design and analysis results, the prototype is trial-produced. The voltage regulation characteristics, no-load characteristics, and external characteristics are obtained. The test results show that the novel hybrid excitation generator performs well.DiscussionThe method can be applied to the development of hybrid excitation generators for automobiles. However, the stability and environmental adaptability of the hybrid excitation generator need to be further studied to speed up its popularization and application.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1733754</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1733754</link>
        <title><![CDATA[Mechanical processing production management technology based on event scheduling and digital management system]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tianshu Huo</author>
        <description><![CDATA[IntroductionMechanical processing production management plays a critical role in optimizing production efficiency and ensuring product quality. Traditional management methods face challenges such as equipment failures, insufficient flexibility in resource scheduling, and low production efficiency. This study proposes a mechanical processing production management technology based on event scheduling and a digital management system to improve production efficiency and order qualification rate while reducing costs.MethodsA digital management system integrating IoT and data analysis technologies was developed to enable real-time monitoring and management of the production process. A multi-objective event scheduling method incorporating the Whale Optimization Algorithm-Grey Wolf Optimizer (WOA-GWO) was adopted to optimize production scheduling. The system employs an event-driven mechanism to capture production line events in real time and dynamically adjust resource allocation and production plans.ResultsOn the Industrial Internet of Things Simulation Dataset (IIoTSD), the recognition accuracy of the system stabilized at around 98%. On the Mechanical Processing Production Historical Dataset (MPPHD), accuracy stabilized at approximately 95%. In practical enterprise applications, the resource utilization rate remained above 90%, and the production cost stayed below CNY 100,000 by the 500th batch. The order qualification rate was maintained at around 98%, and production efficiency remained at approximately 0.95.DiscussionThe proposed approach effectively enhances the level of automation and intelligence in mechanical processing production lines, strengthening the market competitiveness of enterprises. The system demonstrates superior performance in event recognition, resource scheduling, and cost control, providing an intelligent solution for production management. Future work will focus on improving system resilience against external disruptions, enhancing algorithm generalizability, and developing lightweight deployment solutions for small and medium-sized enterprises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1781579</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1781579</link>
        <title><![CDATA[A review of additive manufacturing techniques for wind turbine blade production: capabilities, AI integration, and Scale-Up Potential]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sherif M. Hassanen</author><author>Yassmin Seid Ahmed</author><author>Belal Al Momani</author><author>Abbas Milani</author>
        <description><![CDATA[Hand lay-up and vacuum resin infusion are two labor-intensive, time-consuming, and expensive traditional manufacturing methods used for wind turbine (WT) blades. With the ability to reduce mold manufacturing costs by up to 50%, additive manufacturing (AM) has become an attractive alternative for blade tooling and component fabrication. In 2024, the global market for 3D-printed turbine components reached USD 1.2 billion and is expected to increase to USD 3.8 billion by 2033. This review investigates the integration of AM and artificial intelligence in WT blade manufacturing. AI-assisted defect detection has shown great accuracy in controlled experimental investigations, with some research showing classification accuracies exceeding 90% under controlled laboratory conditions. In several studies, multimodal sensing approaches outperformed single-sensor systems by around 20%. Furthermore, machine learning models have demonstrated excellent prediction ability for composite blade production quality in small-scale experimental datasets. While these findings are promising, further validation under full-scale industrial conditions is required. The synergy of artificial intelligence and additive manufacturing under Industry 4.0 can provide scalable, lightweight, sustainable production as well as enabling defect monitoring, optimization, and adaptive control. Moreover, this integration will improve sustainability through the use of recycled thermoplastic polymers as additive manufacturing feedstocks for blade tooling and small components, thereby reducing energy consumption and material waste compared to thermoset-based processes. However, current limitations include scalability constraints for blades beyond 12 m and a lack of standardized datasets. Research should focus on the development of hybrid artificial intelligence–additive manufacturing frameworks, digital-twin integration, and full-scale validation to accelerate the implementation of these technologies for wind turbine blade manufacturing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1809868</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1809868</link>
        <title><![CDATA[Correction: Analysis of fluid flow across a 2D bluff body in a tandem arrangement with varying aspect ratios near a moving wall]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Pawan Kumar Pant</author><author>Sunil Chamoli</author><author>Naval Pant</author><author>Hitesh Joshi</author><author>Saurav Rana</author><author>Manoj Kumar Pathak</author><author>Varesa Chuwattanakul</author><author>Smith Eiamsa-ard</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1713674</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1713674</link>
        <title><![CDATA[An enhanced subset simulation algorithm integrating importance sampling for structural reliability analysis]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunlong Xu</author>
        <description><![CDATA[IntroductionConducting reliability analyses for engineering problems with small failure probabilities and expensive computational models is challenging. Subset simulation (SS) is an excellent method that has been applied in many fields. However, SS still has several limitations that need to be addressed, such as correlated samples, a large coefficient of variation (COV), the risk of deviating from dominant failure regions, and inaccuracies in problems with multiple failure regions.MethodsIn this paper, a subset simulation algorithm integrating importance sampling is developed to address the aforementioned limitations, focusing on complex reliability problems characterized by low-to-moderate dimensionality and small failure probabilities. First, a seed placement strategy on intermediate limit-state curves is developed to reduce the COV and generate independent samples within each subset. Second, interval estimation combined with clustering algorithms is applied to precisely identify seeds. This strategy is designed to handle problems featuring multiple failure regions while mitigating the risk of divergence from dominant failure regions.ResultsThe performance of the proposed algorithm is demonstrated through seven case studies from the literature, including problems with multiple failure regions, nonlinear problems, system reliability problems, SS counterexamples, and structural reliability problems.DiscussionThe results show that the proposed method provides more accurate and robust failure probability estimates than the other tested methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2026.1774757</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2026.1774757</link>
        <title><![CDATA[Intelligent composite 3D printing: the role of artificial intelligence, machine learning, and in-situ monitoring in next-generation additive manufacturing]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Asad A. Zaidi</author><author>Muhammad Asif</author><author>Abdulrahman Aljabri</author><author>Sohaib Z. Khan</author>
        <description><![CDATA[This narrative review synthesizes recent advances at the intersection of artificial intelligence (AI), machine learning (ML), and composite additive manufacturing (AM) by qualitatively analyzing peer-reviewed journal articles, authoritative review papers, and selected conference literature across materials science, manufacturing, and data-driven engineering. A narrative approach is appropriate for this rapidly evolving, interdisciplinary domain, where heterogeneous platforms, data modalities, and modeling strategies limit strict systematic comparison; therefore, the literature is thematically organized along the composite AM lifecycle to highlight trends, capabilities, and research gaps. Composite 3D printing, embedding fibers or functional fillers within printed matrices, enables lightweight, customizable, high-performance components but remains constrained by anisotropic properties, process instability, and inconsistent quality. The review examines how AI/ML supports (i) feedstock and composite material design, including data-informed formulation screening and property prediction; (ii) in-situ monitoring using vision, thermal, acoustic, and other sensing streams coupled with learning-based defect detection; (iii) adaptive and closed-loop process control, including reinforcement-learning and hybrid controller architectures; and (iv) digital twin frameworks augmented by data analytics and physics-informed models for predictive quality assurance and part performance forecasting. Application-oriented case studies in aerospace, biomedical engineering, automotive/consumer products, and construction are discussed to demonstrate practical impact and industrial relevance. Finally, key limitations, data scarcity and labeling burden, model generalizability across machines/materials, interpretability and trust, and system integration and standardization, are critically assessed, and future directions toward autonomous, sustainable, and secure intelligent composite manufacturing are outlined.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmech.2025.1681872</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmech.2025.1681872</link>
        <title><![CDATA[Experimental study on the road performance of reduced density fly ash–clayey sand subgrade mixtures from the Yellow River floodplain]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiuru Jia</author><author>Qiaoling Ji</author><author>Yu Cheng</author><author>Meixue Wang</author>
        <description><![CDATA[To address challenges in subgrade construction using Yellow River floodplain soils, this study developed a reduced density filler by mixing aged fly ash with locally sourced clayey sand. The research aimed to establish a robust framework for bridging the gap between pavement design specifications (resilient modulus, E0) and construction quality control (compaction degree, K), enabling performance-based quality assurance. Laboratory experiments evaluated compaction characteristics, California Bearing Ratio (CBR), and Laboratory Static Resilient Modulus (E0lab) of the mixtures. Results showed optimal performance at 30%–36% fly ash content, achieving maximum CBR of 23.6% and E0lab of 54.56 MPa. A novel global regression model was established, directly linking CBR to both K and fly ash content (FA%), offering a powerful tool for construction quality assurance. Furthermore, a practical pathway was developed to convert E0lab to field design E0 (via CBR and existing correlations), facilitating the translation of laboratory findings into engineering design. This research culminates in a unified framework for construction quality control, providing recommended K and FA% acceptance windows to guarantee target E0 and CBR values. The developed mixtures are highly suitable for Class II (lower layers) and Class III/IV (top layers) highway subgrades. This study offers robust technical support for sustainable fly ash utilization and performance-driven subgrade construction.]]></description>
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