Permanent magnet synchronous motors are the core components of electric vehicles and widely used in the field of electric vehicles. The existence of vibration will reduce the operating efficiency and service life of the motor, which is the key factor to determine whether the motor is running normally. In this paper, a certain type of permanent magnet synchronous motor is taken as the test object. The permanent magnet synchronous motor is divided into three substructures: stator, rotor and shell. The finite element modal results are obtained respectively. The modal parameters of the whole PMSM are calculated by using the substructure modal synthesis method. The weak links of the motor are found out and the structure optimization is carried out. Through the calculation of the modal analysis, it can be seen that each vibration mode of the optimized motor has been reduced, and the amplitude difference between the 2nd order and the 5th order is the largest. Finally, the modal test is carried out on the motor using the exciter method, the test results are analyzed, and the modal parameters of the motor are obtained through the frequency domain analysis method, the FEA result is confirmed by the modal test of the motor.

Panoramic segmentation of 3D point clouds is an essential and challenging technology for robots with 3D detection and measurement capabilities. In order to fuse the color information of 2D image pixels with the spatial position information of the 3D LiDAR point cloud, it is necessary to establish the corresponding relationship between the RGB of pixels and the XYZ position of the 3D LiDAR point cloud. We present Double Texture Neighbor LiDAR Segmentation (DTNLS) in this letter. Double texture refers to the color texture of the image and the point cloud texture of the 3D LiDAR. The DTNLS method first uses the color texture feature of the image to segment the pixels and then obtains the clustering center and segmentation boundary outline. 3D LiDAR point cloud texture segmentation takes the clustering center obtained above as the diffusion source, diffuses outwards along the ring LiDAR line, finds the LiDAR point cloud texture boundary features near the image segmentation boundary contour, and finally realizes 3D point cloud segmentation. We carried out quantitative analysis experiments, and the results showed that compared with the best results among the existing mainstream segmentation methods, the proposed DTNLS method improved the accuracy of pedestrian segmentation by 32.2%. The recall improved by 20.12% in pedestrian segmentation. IoU improved by 48.8% for pedestrians. We conducted empirical studies on public datasets and our datasets to demonstrate that DTNLS has broad applicability and better performance in 3D point cloud segmentation than the previous latest techniques, without the need for any 2D images and 3D point cloud training data.

As a novel building forms, prefabricated buildings offer unique advantages in terms of safety and speed. Focusing on the problems of target tracking in the threaded sleeve production line of prefabricated buildings, such as low accuracy rate, slow recognition speed, and difficulty in dealing with object occlusion and environment changes, a target tracking and detection approach based on the improved DeepSort algorithm is proposed. The real-time tracking of the equipment of the prefabricated building threaded sleeve production line is realized. The improved YOLOv5 replaces the detection algorithm in the track phase of the original DeepSort algorithm, which is used for real-time tracking and detecting the equipment. The experimental results show that the tracking accuracy of the improved algorithm is 88%, 70.5%, and 64.8% for three videos of different lengths, respectively. The tracking detection frame rate reached 71.95 fps in the three experiments of video tracking. The improvement of the frame rate of the improved algorithm is over 100% compared with the traditional tracking algorithms. The improved algorithm enhances the accuracy of target tracking in prefabricated building threaded sleeve production line and improves the production efficiency of prefabricated building threaded sleeve production line effectively.

This paper presents a hybrid experimental-finite element analysis (FEA) methodology for the vibration suppression of thin-walled enclosures in precision equipment. The core of this approach is the development of a high-fidelity dynamic model, which is rigorously updated and validated through experimental modal analysis (EMA). Utilizing impact hammer testing, the modal parameters (natural frequencies and mode shapes) of the enclosure were measured with high repeatability. The initial FEA model was then calibrated against the experimental results, achieving an exceptional agreement with frequency errors of less than 5% for the first four modes, which confirms the accuracy of the identified vibration transmission paths. This validated model precisely pinpointed the critical vibration modes and localized the weak areas, including the top cover and side panels, that were susceptible to resonance within the operational frequency band (180-240 Hz). Guided by these insights, targeted structural modifications were implemented, featuring a sealed screw fastening process, an R25mm fillet bend on the top cover. The effectiveness of these optimizations was quantitatively assessed through the updated FEA model, demonstrating a successful shift of the critical modal frequencies away from the excitation band with a separation margin exceeding 10%. Consequently, the overall vibration amplitude was reduced by 42.3%, and stress concentration was mitigated by 28%. The study underscores the efficacy of the hybrid modelling approach as a powerful tool for the design and troubleshooting of dynamic systems in precision engineering.