The solidification cracking behavior in laser welds of steel/copper dissimilar metals was systematically investigated. T2 copper and SUS304 stainless steel were used in the study. The results showed that the occurrence of solidification cracking in welds was the synergistic effect of ε phase liquation, inclusions and composition segregation. During the welding process, the liquation of grain boundaries substantially reduced the cohesion between adjacent grains, as well as the resistance for intergranular crack propagation. The composition segregation inside the grains could induce lattice distortion, thus reducing the plastic deformation capacity of the material itself and concurrently increasing the susceptibility to cracks. In addition, an effective solution for inhibiting solidification cracking was proposed by using an oscillating laser, and the inhibition mechanism was further discussed. Laser oscillating welding significantly promoted grain refinement, solute diffusion and the formation of uniformly distributed ε-Cu precipitated phases in welds. It can improve the intergranular bonding, reduce the susceptibility to solidification cracking and increase the resistance to plastic deformation. The tensile strength of joints using laser oscillating welding is 251 MPa, 35.7% more than 185 MPa using laser welding. Meanwhile, the strain of joints using laser oscillating welding is 3.69, a 96% increase compared to 1.88 using laser welding.

As a representative type of superalloy, Inconel 718 is widely employed in aerospace, marine and nuclear industries. The significant work hardening behavior of Inconel 718 can improve the service performance of components; nevertheless, it cause extreme difficulty in machining. This paper aims to investigate the influence of chamfered edge parameters on work hardening in orthogonal cutting of Inconel 718 based on a novel hybrid method, which integrates Coupled Eulerian-Lagrangian (CEL) method and grain-size-based functions considering the influence of grain size on microhardness. Orthogonal cutting experiments and nanoindentation tests are conducted to validate the effectiveness of the proposed method. The predicted results are highly consistent with the experimental results. The depth of work hardening layer increases with increasing chamfer angle and chamfer width, also with increasing feed rate (the uncut chip thickness). However, the maximum microhardness on the machined surface does not exhibit a significant difference. The proposed method can provide theoretical guidance for the optimization of cutting parameters and improvement of the work hardening.

Magnetic pole position detection is the core of the closed-loop control system of the permanent magnet linear synchronous motor (PMLSM), and its position estimation accuracy directly affects control performance and dynamic response speed. In order to solve the problem of the increased estimation error of magnetic pole position caused by magnetic field distortion at the end of PMLSM while also considering the cost of control hardware, the paper uses four linear Hall sensors as magnetic pole position detection components and adopts an optimized estimation algorithm to improve the dynamic performance of the motor. Firstly, a numerical simulation of the magnetic field of poles was conducted using Ansoft Maxwell software, and combined with theoretical analysis, the optimal installation position range of four linear Hall orthogonal placements relative to the motor was obtained. Meanwhile, based on the existing vector tracking position observer, an improved observer detection model is proposed. The Matlab/Simulink software was used to compare the Hall-based detection model with the Hall-based improved observer detection model, verifying the feasibility of the improved detection algorithm. Finally, the rationality of the spatial layout design of linear Hall and the feasibility of improving the estimation algorithm were verified through experiments.

3D vision-based groove detection is playing a critical role in enabling autonomous recognition. However, most existing interference evaluation strategies rely on height-based statistics or handcrafted heuristics, which (i) confuse genuine groove geometries (e.g., deep gaps and rounded corners) with noise and (ii) are sensitive to measurement scale and scanning configurations, making parameter tuning unreliable across scenes. To address this challenge, this paper proposes a novel method for evaluating the degree of interference in groove detection data, providing a reliable basis for the adaptive adjustment of algorithm parameters. The method leverages the angles of reconstructed triangular patches to assess the interference level in groove 3D detection data and computes the eigenvalues of the covariance matrix of these angles, establishing a rotationally invariant model for interference quantification. Experimental results show that the proposed method outperforms traditional methods, identifying more regions of high dispersion and demonstrating better adaptability to common groove features, such as deep gaps and rounded corners. By exploiting geometric invariance as a form of symmetry, the proposed eigenvalue-based dispersion descriptor provides a robust and coordinate-independent criterion for interference evaluation. Quantitatively, across multiple real industrial datasets, the proposed descriptor achieves an average 30.99% improvement in identifying severely interfered regions compared with the mainstream height-difference-based evaluation baseline.