This paper proposes an analytical solution and isogeometric analysis numerical approach for buckling analysis of size-dependent beams based on a reformulated strain gradient elasticity theory (RSGET). The superiority of this method is that it has only one material parameter for couple stress and another material parameter for strain gradient effects. Using the RSGET and the principle of minimum potential energy, both non-classical Euler–Bernoulli and Timoshenko beam buckling models are developed. Moreover, the obtained governing equations are solved by an exact solution and isogeometric analysis approach, which conforms to the requirements of higher continuity in gradient elasticity theory. Numerical results are compared with exact solutions to reveal the accuracy of the current isogeometric analysis approach. The influences of length–scale parameter, length-to-thickness ratio, beam thickness and boundary conditions are investigated. Moreover, the difference between the buckling responses obtained by the Timoshenko and Euler–Bernoulli theories shows that the Euler–Bernoulli theory is suitable for slender beams.

Given that the heat treatment states of the base metal have a great influence on the surfacing repair layer, this paper carried out a feasibility study for the remanufacturing of the failed cutter rings of TBM disc cutters with uniform wear (hereinafter referred to as normally-worn ring) using the gas metal arc welding technology (GMAW). Firstly, this paper developed a heat treatment process route for H13 steel cutter rings. Secondly, the heat treatment process is numerically analyzed based on the developed route, and the rationality of the route is verified from the distribution characteristics of temperature, phase, and stress fields. Subsequently, heat treatment tests were carried out, and the physical and mechanical properties of the base metal samples prepared under laboratory conditions were evaluated respectively and systematically. Based on the comprehensive performance evaluation value calculated by the weighted comparative analysis method, it was clear that the comprehensive performance of the quenched base metal samples was 7.6% higher than that of the engineering cutter ring interior. Therefore, it is reasonable to replace the failed engineering cutter rings repaired under laboratory conditions with the prepared samples as economical alternatives. Finally, the remanufacturing of the base metal samples using GMAW was carried out, and then the remanufacturing performance of the base metal samples was analyzed. The study concluded that the comprehensive performance of the surfacing repair layer was slightly lower than that of the engineering cutter ring edge (4.1%), thus proving that the idea of surfacing remanufacturing of the normally-worn ring proposed in this paper was basically feasible.

Different protection states have different protection effects on bogie traction motor bearings under road transportation conditions, which directly affects the stability and safety of later vehicle operations. The motor bearing monitoring system usually adopts the time-frequency domain and other indicators to accurately describe its state, and the selection of specific indicators usually originates from the experience of domain experts. To explore the weak and effective features hidden in the data more deeply, this paper researches feature selection and fusion, and proposes a hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment. Firstly, a platform is built to collect multiple sensor signals of traction motor vibration during road transportation and the time-frequency domain feature set is extracted. Then, by comparing the feature extraction effects of 10 single-measure models, a hybrid multi-measure feature selection model and its evaluation indexes, which comprehensively consider the combined distance measure, correlation, and information measure, are constructed to optimize the best hybrid multimetric and the feature set corresponding to the optimal strategy. To perform multi-sensor feature fusion, the PSO-optimized UMAP algorithm is used for feature fusion of the feature set and compared with eight other algorithms. Finally, the results of feature selection and fusion are input into eight classifiers for performance comparison, and the method achieves more than 85% accuracy in distinguishing different protection states, obviously due to the original feature subset. The experimental results prove the effectiveness of the method proposed in this paper, and it provides a theoretical basis and technical reference for the protection scheme of the traction motor and the design and selection of bearings.

The metal thickness uniformity of the micro-electroforming process determines the structural accuracy, surface finish, and functional characteristics, which directly affect the quality, performance, and reliability of the final parts. However, due to the non-uniformity of the electric field distribution, the problem of thickness non-uniformity in the micro-electroforming process has always been one of the key challenges limiting its application and development. The auxiliary cathode structure can improve the uniformity of electroforming thickness by regulating the cathode electrodeposition rate and electric field distribution and is widely used in the micro-electroforming process. In this study, with the micro-array metal die core as the research object, based on finite element simulations, the effect of the geometrical parameters of the auxiliary cathode on the thickness uniformity of electroforming is analyzed in depth. This study introduces the optimization method of the BP neural network and the NSWOA (Non-dominated Sorting Whale Optimization Algorithm) and achieves a significant improvement in the unevenness of electroforming thickness by designing and adjusting the parameters of the position, width, and shape of the auxiliary cathode. The unevenness of the microarray metal mold core is reduced to 4% from 475% without the auxiliary cathode, using the auxiliary cathode structure designed with this method.

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency.