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To optimize the welding process for dissimilar metals, this study explores the influence of defocus amount on the microstructure and properties of laser-welded joints between Q235 and 304 stainless steel. Welding experiments were performed using an EFE-LWM-1500 laser welding machine at a fixed laser power of 1200 W with five defocus amounts: –10 mm, –5 mm, 0 mm, +5 mm, and +10 mm. The welded joints were characterized by optical microscopy, X-ray diffraction (XRD), universal tensile testing, scanning electron microscopy (SEM), and electrochemical workstation to analyze the microstructure, phase composition, tensile strength, fracture morphology, and corrosion resistance. Metallographic analysis shows that the 304 stainless steel side is dominated by austenite, the weld center exhibits lath martensite, and the heat-affected zone of Q235 carbon steel contains Widmanstatten structure, pearlite, and acicular ferrite. The weld grains are most uniform and fine at 0 mm defocus. XRD confirms the presence of Fe-Ni solid solution in the joint. Mechanical tests reveal that the average microhardness of the weld reaches a peak of 373.05 HV and the tensile strength reaches a maximum of 309.46 MPa at 0 mm defocus. Electrochemical tests show that the self-corrosion current density is as low as 2.24×10-5 A/cm2 and the capacitive arc radius is the largest under this condition, indicating the best corrosion resistance. SEM analysis of the fracture surface shows typical necking and dimpled structures, confirming ductile fracture. The defocus amount significantly regulates the properties of Q235/304 dissimilar steel laser-welded joints. A defocus amount of 0 mm achieves the optimal microstructure and comprehensive properties, providing an important reference for optimizing laser welding parameters of dissimilar metals.

Steep, highly fractured rock slopes, characterized by lithological heterogeneity, intense fracturing, and sensitivity to rainfall-induced hydraulic perturbations, pose challenges that are not covered by classical sliding-surface models. This study integrates multiphysics monitoring, laboratory experiments, and probabilistic inversion to investigate a representative slope at the Luanchuan Longyu open-pit mine. A positive feedback loop was found between seepage and fracture development: elevated pore pressure induced by rainfall or increased groundwater levels drives fracture propagation, enhances permeability and seepage velocity, reduces effective stress, and initiates a self-accelerating “seepage → fracture → rock weakening” chain, even in the absence of a continuous slip surface. Lithological analysis reveals that marble and gneissic granite exhibit high strength (UCS ≈ 57-58 MPa, c≈ 6.2-6.8 MPa, ϕ ≈ 39°-43°), whereas quartz-mica schist is a highly weathering-sensitive weak layer (saturated UCS = 9.78 MPa, with 35 % strength loss and SDI = 58.96 % after 11 wetting-drying cycles). Parameter back-analysis (with 80 % confidence intervals) reveals schist (c = 24.5 kPa, ϕ = 34.4°) as the dominant weak stratum and faults (c = 8.9 kPa) as potential slip surfaces, clarifying the lithology-structure controls on failure patterns. Rainfall infiltration triggers a nonlinear hydraulic response and preferential toe flow, causing pore pressure to propagate downward and shifting slope degradation from surficial to whole-slope failure-a dynamic "surface-to-depth" progression modeled in this study. These findings provide a quantitative mechanistic framework for assessing steep, heterogeneous slopes and support intelligent hazard mitigation through rainfall thresholds and real-time monitoring.

Responsive polymers can generate various self-sustained motions through the tuning of their geometric configurations, external stimuli, and boundary constraints. Research and innovation of novel self-sustained motions can broaden the scope of application for self-sustained active machines. In this paper, a novel dynamic model for the self-excited vibration of a thermally expandable cantilever beam driven by a steady temperature difference is constructed. The governing equation for the self-excited vibration of the beam is derived and solved using the modal superposition method. Numerical calculations reveal that the beam has two typical motion states, namely, the static state and the self-excited vibration state. The self-excitation mechanism is explained by the coupling between beam motion and the periodic switching of the thermally induced bending torque. The effects of each system parameter on the amplitude of the vibration in steady state are further investigated quantitatively, and the critical values for triggering the beam self-excited vibration are identified. Furthermore, the period of the self-excited vibration in steady state is almost unaffected by environmental parameters. The self-excited vibration cantilever beam holds promising potential for applications in soft robotics, energy harvesting, active motors, and self-sustained machinery.

Drawbacks of continuous flexible manipulators, such as insufficient trajectory tracking accuracy and obvious flexible vibration, were targeted, and an integrated hierarchical strategy of trajectory replanning and disturbance rejection control was proposed for cable-driven flexible manipulators. A STO-MPC (Stochastic Trajectory Optimization Model Predictive Control) framework was constructed. Probabilistic obstacle avoidance constraints and gradient-independent solving mechanisms were introduced to tackle the high computational delay and poor dynamic adaptability of traditional planning approaches, while stiffness constraints and residual vibration suppression requirements of flexible structures were fully satisfied. A cooperative control framework of ADO-RITSMC (Adaptive Disturbance Observer-Rapid Integral Terminal Sliding Mode Control) was established. Model-free real-time compensation for multi-source disturbances was realized, finite-time convergence of tracking errors was achieved by the improved terminal sliding mode, and residual vibration of flexible links was effectively suppressed accordingly. Comprehensive comparative tests were conducted on standard O-shaped trajectories and high-curvature V-shaped trajectories, with GO-MPC (Gaussian Observer-based Model Predictive Control), APF-MPC (Artificial Potential Field-based Model Predictive Control) sliding mode algorithms. Experimental results demonstrated that STO-MPC can achieve the lowest peak computation time, completes the convergence of obstacle state estimation within 0.5 s, and yields a steady-state velocity estimation error of 0.0015 m/s. Its trajectory tracking RMSE (Root Mean Square Error) is 18.7 % and 36.8 % lower than that of GO-MPC and APF-MPC respectively, delivering superior real-time performance, estimation stability and tracking accuracy. The proposed ADO-RITSMC reduces vibration amplitude by 16.7 % with a peak vibration acceleration of –1.0 g, and exhibits faster vibration attenuation and slighter trajectory oscillation during dynamic obstacle avoidance, which fully verifies the hierarchical collaborative advantages of STO-MPC trajectory replanning and ADO-RITSMC vibration suppression.

To solve the engineering problems of severe vibration in the dehydration stage, insufficient reliability of load-bearing components, and the difficulty in balancing lightweight design and dynamic performance of industrial washing machines, mechanical performance analysis of key load-bearing components and optimization of vibration isolation systems were conducted. Dynamic modeling, finite element simulation and multi-objective optimization methods were integrated under extreme working load conditions. The dynamic model of the vibration isolation system was established by the Lagrange method. The rigid-flexible coupling simulation model of the cabinet and the refined finite element model of the inner cylinder were constructed. The Kriging surrogate model was adopted to fit nonlinear correlations between structural design variables and mechanical responses. Optimization results quantitatively demonstrate that the cabinet achieves an 11.0 % weight reduction and the inner cylinder realizes a 9.8 % mass cut without exceeding the original maximum stress and fundamental frequency constraints. After optimizing the matching of suspension spring stiffness and damping coefficient, the resonance peak amplitude of the inner cylinder center of mass vibration decreased by 58 %, effectively suppressing the equipment resonance amplification phenomenon and improving the dehydration stability and efficiency. This study provides a complete simulation-surrogate multi-objective optimization workflow for heavy laundry equipment bearing structures and vibration isolation systems under extreme service loads.

The landing gear system is one of the critical subsystems of an aircraft, directly affecting the safety of takeoff and landing. The landing gear retraction and extension mechanism (LGREM) exhibits strong nonlinearity, with complex coupling interactions among its parts. Using multi-rigid-body kinematics and dynamics to calculate stresses and strains of the LGREM would be highly challenging. This paper proposes a coupled dual-extreme value response surface method (CDEVRSM) that considers parameter coupling between parts. By taking the dual- extreme values obtained from finite element (FE) transient structural simulations as output responses, and using overload coefficient, material density, and gravitational acceleration as random variables, a response surface function (RSF) is constructed for strength reliability analysis of the LGREM. Employing the Monte Carlo algorithm with 1,000,000 large-scale sampling, the reliability of the LGREM is calculated to be 99.9844 % by statistical calculation. Its relative error was reduced by approximately 61 % compared with the extreme value response surface method. Moreover, compared to approximately 1000 hours required by FE methods, the CDEVRSM reduces computation time to approximately 10 hours, greatly improving computational efficiency. The sensitivity of three random input variables to the reliability of the LGREM was analyzed, and the main and secondary factors affecting the reliability of the LGREM are ranked in descending order of importance as overload coefficient, material density, and gravitational acceleration.

To address issues such as the large number of parameters, high computational complexity, and inadequate real-time performance in existing CNN-Transformer hybrid fault diagnosis models, we propose a lightweight fault diagnosis framework, LWConvFormer. This framework comprises two core innovative modules: a dynamic separable multi-scale convolutional module that employs a gated network for adaptive feature extraction, thereby reducing computational load while enhancing adaptability to complex fault modes; and a broadcast self-attention module that substitutes traditional matrix multiplication with broadcast operations, thereby decreasing computational complexity from a quadratic to a linear level. Experimental results based on the planetary gearbox at Xi'an Jiaotong University and the QPZZ-II type rotating machinery test bench demonstrate that LWConvFormer maintains excellent diagnostic performance across various noise levels. The number of parameters and computational load are reduced by a factor of 6 to 10 compared to mainstream methods, while the training speed increases by nearly 7 times. This framework effectively balances diagnostic accuracy, model lightweighting, and noise resistance, offering an efficient solution for real-time fault diagnosis in industrial settings.

This paper presents the development of a wearable system for human motion monitoring based on an inertial measurement unit (IMU). The proposed device enables real-time acquisition of angular velocity, linear acceleration, and orientation parameters of a body segment. An experimental prototype was implemented using an IMU sensor, Arduino Nano, and a data recording module. Laboratory tests focused on dorsiflexion and plantarflexion movements of the ankle joint. The results demonstrate that the system can accurately capture motion parameters and reflect changes in the Pitch angle corresponding to these movements. The proposed approach can be applied in motion analysis and rehabilitation monitoring. Future work includes integration of EMG and force sensors to extend the system functionality.