The movement of the gas–liquid interface caused by the movement of the bubble position will have an impact on the starting conditions for particle migration. This article quantifies the influence of moving bubbles on the starting conditions of particle migration in non-Newtonian fluids, and it aims to better understand the influence of bubbles moving in non-Newtonian fluids on particle migration to achieve more effective control. First, the forces and moments acting on the particles are analyzed; then, fluid dynamics, non-Newtonian fluid mechanics, extended DLVO (Derjaguin Landau Verwey Overbeek theory), surface tension, and friction are applied on the combined effects of particle migration. Then, we reasonably predict the influence of gas–liquid interface movement on particle migration in non-Newtonian fluids. The theoretical results show that the movement of the gas–liquid interface in non-Newtonian fluids will increase the separation force acting on the particles, which will lead to particle migration. Second, we carry out the particle migration experiment of moving bubbles in non-Newtonian fluid. Experiments show that when the solid–liquid two-phase flow is originally stable, particle migration occurs after the bubble movement is added. This phenomenon shows that the non-Newtonian fluid with bubble motion has stronger particle migration ability. Although there are some errors, the experimental results basically support the theoretical data.

In order to detect the abrasion of rails, a new point cloud alignment method combining 4-points congruent sets (4PCS) coarse alignment based on internal shape signature (ISS) and K-dimensional iterative closest points (KD-ICP) fine alignment is proposed, and for the first time, the combined algorithm is applied to the detection of rail wear. Due to the large amount of 3D rail point cloud data collected by the 3D line laser sensor, the original data are first downsampled by voxel filtering. Then, ISS feature points are extracted from the processed point cloud data for 4PCS coarse alignment, and the feature points are quantitatively analyzed, which in turn provides good alignment conditions for fine alignment. Then, the K-dimensional tree structure is used for the near-neighbor search to improve the alignment efficiency of the ICP algorithm. Finally, the total rail wear is calculated by combining the fine alignment results with the wear calculation formula. The experimental results show that when the number of ISS feature points extracted is 4496, the 4PCS coarse alignment algorithm based on ISS feature points is higher than the original 4PCS algorithm as well as the other algorithms in terms of alignment accuracy; the ICP fine alignment algorithm based on the kd-tree is less than the original ICP algorithm as well as the other algorithms in terms of the time consumed. Further, the proposed new ISS-4PCS + KD-ICP two-stage point cloud alignment method is superior to the original 4PCS + ICP algorithm both in terms of alignment accuracy and runtime. The combined algorithm is applied to the detection of rail wear for the first time, which provides a reference for the non-contact rail wear detection method. The high accuracy and low time consumption of the proposed algorithm lays a good foundation for the calculation of rail wear in the next step.

Efficient packing of irregular mechanical parts in limited container space is essential for reducing transportation and storage costs in automated manufacturing. This study focuses on eccentric-shaped parts characterized by geometric asymmetry, multiple orientations, and local irregularities, and proposes a two-stage three-dimensional packing strategy. In the first stage, an optimal single-layer layout is generated using a heuristic algorithm that combines grid scanning with a gravity-drop principle and tabu search to optimize part positions and orientations. In the second stage, the optimized layer template is vertically replicated with buffer layers to enhance stacking stability, ensuring feasible and non-overlapping arrangements. Comparative experiments with Best Fit, BLF, LHL, and Random methods show that the proposed approach increases average space utilization by 8.3%, 8.8%, 8.1%, and 15.5%, respectively, while maintaining high stability and reasonable computation time. The results demonstrate that this method achieves dense and stable packing, offering an effective solution for intelligent packing and automated production of irregular parts.

This study investigates the structural optimization, lightweight design, and fatigue life influencing factors of the excavator working device through comprehensive static and dynamic finite element analysis (FEA). Modal analysis, spectral analysis, and vibration transfer path identification were conducted to assess the vibration response and fatigue failure risks at key joints such as the forearm pinholes. The inclusion of figures, such as vibration paths and stress distribution plots (see Figs. 3-8), supports the findings. The research provides a theoretical basis for assessing the structural reliability and fatigue life of long-arm excavators exceeding 25 meters.