Introduction: Balancing active slip prevention and crush avoidance is challenging for fragile-object grasping. Conventional end-to-end systems often confound Out-of-Distribution (OOD) perceptual errors with underlying control deficiencies, obscuring true boundary robustness. Methods: This study proposes a risk-aware Reinforcement Learning (RL) framework and a decoupled evaluation architecture. A convolutional neural network-long short-term memory network validates temporal tactile slip representations offline, whereas the control loop is isolated from perceptual errors. A fragility sweep tightens the crush limit from 25.0 to 18.0 N using analytically computed proxy risk states and composite rewards to evaluate simulated policy boundaries. Policy training was assessed using three independent seeds, and each boundary condition was evaluated over 200 episodes. Results: At 25.0 N, Sparse RL achieved a success rate of 83.5%. However, when the limit decreased to 19.0 N or lower, the same subset of trajectories collapsed into crush failures, yielding a 0% success rate and 83.5% crush-failure rate. Full RL retained a success rate of 62.0% at 18.0 N. Heuristic reflexes were conservative, while closed-loop tests with synthetically rendered tactile observations revealed severe performance degradation under OOD conditions. Discussion: The decoupled architecture isolates control mechanics, demonstrating that unconstrained policies rely on transient high forces. In the simulated boundary tests, continuous risk optimization produces more stable contact than rigid heuristic strategies. Conclusion: Explicit risk constraints are critical for boundary control. The proposed framework provides a simulation-based paradigm for identifying control-degradation mechanisms, although real- robot validation remains a future work direction.
Introduction: Traditional forecasts of end-of-life vehicle (ELV) flows treat all cars as a single homogeneous stock, ignoring the emerging split between fuel vehicles and new-energy vehicles (NEVs). This omission becomes critical as NEV sales soar and their batteries enter the recycling stream. To address this gap, this research establishes a high-precision forecasting framework for the ELV volume of NEVs in China, tackling critical data scarcity and the lack of classified prediction between fuel and electric vehicles. By integrating sales-driven lifetime modeling with macroeconomic scenario sensitivity, the study develops and validates a NEV-oriented scrappage framework to improve prediction accuracy and provide a robust foundation for recycling research and national resource supply planning. Methods: Fuel-vehicle stock was benchmarked against NEV stock, historical NEV sales were fitted (exponential trend; R2=0.9876), and a Weibull failure-rate model was integrated with sales to estimate NEV retirements; additionally, quarterly ownership data were expanded using SMOTE, growth stabilization was characterized via a Grey Model, and a Gompertz curve was fitted to link per-capita vehicle ownership with GDP per capita to enable scenario-based dynamic scrappage forecasting. Results: Compared with the traditional Gompertz model, which often overlooks the distinct scrapping characteristics of NEVs, the proposed method reduces the forecasting bias. The integration of Weibull-based survival analysis enables the model to achieve a significant improvement in precision, with a low relative error of 8.999%. Incorporating SMOTE-enhanced ownership data, Grey- Model-identified stabilization periods, and a Gompertz ownership-GDP relationship improved data completeness and enabled dynamic scrappage predictions under different economic growth scenarios, offering stronger scenario adaptability than traditional approaches. Discussion: The findings provide a critical foundation for future recycling research. By enabling precise forecasting, the model significantly impacts the supply chain by stabilizing the supply of raw materials for the NEV industry and boosting metal resource efficiency. This approach directly catalyzes the transition from linear consumption models to a closed-loop circular economy. Ultimately, these contributions align with the broader strategic objectives of carbon neutrality and sustainable development, providing policymakers and industrial stakeholders with the quantitative support required to advance greener practices within the automotive sector. Conclusion: The proposed framework provides a validated and flexible methodology for forecasting NEV retirement volumes in China, supporting recycling research, stabilizing raw-material supply, and improving metal resource efficiency across the NEV supply chain; future work should refine classification by distinguishing between battery electric vehicles and plug-in hybrid electric vehicles to further enhance accuracy and policy relevance. For future research, vehicle categories should be refined by distinguishing between BEVs and PHEVs. Additionally, separate failure parameters should be developed for private and commercial vehicles to account for different usage intensities.
Introduction: This study aims to address the problems of low energy storage density and insufficient structural reliability in magnetic levitation flywheel rotors, which are critical for enhancing power grid stability and power supply quality in physical energy storage systems. Methods: To address the low energy storage density in magnetic-levitation flywheel rotors, a collaborative optimization method based on a PSO-GA two-layer hybrid strategy was proposed. This method uses the outer PSO to adaptively tune the hyperparameters of the inner GA, which performs size-feature optimization. The method takes the thickness and interlayer interference of the threelayer composite material as design variables, and the geometric size constraints, interference assembly requirements, and the Tsai-Hill strength failure criterion as constraints. The optimization model is constructed to maximize the energy storage density. Finally, the effectiveness of the optimization results was verified through finite element analysis and rotor dynamics simulation. Results: After 300 iterations of PSO-GA, the rotor energy storage density increased by 11.28% and the total energy output by 21.15%. At the rated speed of 38,000 rpm, both the maximum stress and the critical speed margin remained within safety limits. Discussion: These improvements validate the effectiveness of the hybrid algorithm for high-speed rotor design. The satisfaction of stress and critical speed constraints under rated conditions confirms reliability, offering a practical reference for future energy storage rotor optimization. Conclusion: This method provides a theoretical basis and engineering application data reference for the optimized design of high-efficiency flywheel energy storage.
Introduction: Wire Arc Additive Manufacturing is increasingly used for producing medium- and large-scale metallic components due to its high deposition rate and efficient material usage. Despite these advantages, achieving consistent bead geometry and defect-free deposition depends strongly on the selection of process parameters. The present study aims to examine and optimize key input parameters that affect the formation of a single-layer weld bead in the WAAM process. Methods: Single-layer weld beads were fabricated using SS316L stainless steel MIG filler wire. The main process variables considered were shielding gas flow rate, open-circuit voltage, and welding speed. The quality of the deposited bead was evaluated in terms of height-to-width ratio and microhardness. Experiments were designed using a Taguchi L9 (3⁴) orthogonal array, and the results were analyzed with Minitab 17 and ANOVA F-test to determine the influence of each parameter and identify suitable operating conditions. Results: The experimental results indicate that variations in SGFR, OCV, and WS have a noticeable effect on bead geometry and hardness. Certain parameter combinations resulted in improved bead shape with a more uniform height-to-width ratio, along with better hardness values. The analysis also highlighted the relative significance of each parameter in controlling the deposition characteristics. Discussion: The study shows that proper adjustment of process parameters is essential for maintaining bead stability and achieving desirable mechanical properties. The interaction between heat input and material deposition plays a key role in defining bead shape and hardness. The outcomes provide useful guidance for selecting process conditions in WAAM applications to improve build quality and consistency. Conclusion: The investigation demonstrates that optimizing the shielding gas flow rate, opencircuit voltage, and welding speed improves weld-bead characteristics in WAAM. The Taguchi method proved effective in identifying suitable parameter settings, supporting better control of the deposition process and enhancing overall performance.
Introduction: Lubrication failure restricts the service life of artificial joints, and surface texturing improves tribological performance by modulating surface micro-topography. This study aims to achieve precise fabrication of micro/nano-scale surface textures on artificial joint surfaces and to promote the enrichment of lubricating biomacromolecules within these regular textures to enhance lubrication performance. Methods: Nano-Fe3O4 particles were synthesized via co-precipitation coupled with hydrothermal treatment. These particles were dispersed into a photosensitive resin and subsequently cured under simultaneous magnetic field induction and UV irradiation. A 0.3 wt% chitosan aqueous solution was adopted as a lubricant for tribological tests. Results: The synthesized nano-Fe3O4 particles exhibited a uniform average diameter of 30 ± 5 nm. Under a magnetic field of 0.71 T and UV irradiation at 400 W/m2, the magnetically responsive photosensitive resin slurry containing 30 wt% nano-Fe3O4 formed ordered, chain-like micro-nano arrays aligned along the magnetic field direction. In contrast, slurries with 10 wt% and 20 wt% nano- Fe3O4 yielded only random particle dispersion. Tribological tests demonstrated that lubricant accumulation within the regular surface textures effectively reduced the average friction coefficient. Discussion: This methodology enables the precise fabrication of ordered micro- and nanostructured surface textures, thereby enhancing interfacial lubrication and offering a viable strategy to extend the service life of artificial joints. Conclusion: Magnetically assisted photopolymerization utilizing nano-Fe3O4 particles represents an effective approach for fabricating functional surface textures. The resulting micro-nano textures effectively reduce the friction coefficient and enrich lubricating biomacromolecules, thereby improving the tribological performance and service durability of artificial joints.
