The A* algorithm is an effective method for path planning; however, it has certain drawbacks, such as a high number of turns, low planning efficiency, and redundant searches. To address these issues, this paper proposes an improved bidirectional A* global path planning algorithm based on a variable neighborhood search strategy, named VNS-BA*. The new algorithm first employs an 8-11-13 neighborhood search method for node expansion. Then, the bidirectional search strategy is optimized by using the current nodes of the opposite path and the global target point, enabling the two paths to meet in the middle of the map. Finally, redundant turns are removed from the path, and cubic spline interpolation is applied to achieve local smoothing at the turns. The effectiveness of the improved algorithm was validated on different maps and compared with A* and its three derived improved versions. The simulation results indicate that VNS-BA* shows significant improvements in terms of the number of path turns, turn angles, and planning efficiency.

This study involved the preparation of dense Ti-10wt.%Mo alloys using selective laser melting (SLM) with a powder combination of pure titanium (Ti) and pure molybdenum (Mo). Integrating temperature stress numerical simulations and actual data elucidates the correlation between linear laser energy density and residual stress. The impact of linear energy density on the surface roughness, densification behavior, microstructural development, and mechanical properties of SLM-processed Ti-10Mo components was also examined. As linear energy density diminished from 0.125 J/mm to 0.233 J/mm, surface roughness reduced from 18.2 μm to 4.4 μm, while relative compactness increased from 94.9% to 99.8%, respectively. It is necessary to reduce the friction between the puncture needle or implant needle and human tissue, enhancing comfort and precision. The microstructural investigation revealed that SLM-processed Ti-10Mo alloys consist of a phase combination of hexagonal tight-packed (hcp) α-Ti and body-centered cubic (bcc) β-Ti, with heterogeneous conchoidal microstructures found in the samples. Furthermore, as the laser energy input increased, Mo powder particles were mostly fully melted, leading to a significant rise in the microhardness value. The as-built Ti-10Mo alloys exhibited a high ultimate tensile strength of 860 MPa and an elongation of 32.9% at a linear laser energy density of 0.15 J/mm, with the fracture morphology indicating a mixed fracture mode mostly characterized by ductile fracture. This research can enhance the prospective bio-application of Ti-Mo alloys.

A bearing intelligent fault diagnosis method based on an improved convolutional neural network is proposed to address the problems of high noise, difficult fault feature extraction, and low fault diagnosis recognition rate in rolling bearing vibration signals collected under complex working conditions. Firstly, in the data preprocessing stage, the wavelet denoising method is used to preprocess the data to obtain higher-quality signals. Then, the convolutional neural network LeNet-5 model was improved through batch normalization, Dropout, and L2 regularization methods. The wavelet denoised signal was input into the optimized LeNet-5 model to achieve more accurate fault diagnosis output for rolling bearings. Finally, to demonstrate the generalization ability of the model, this paper uses publicly available rolling bearing data from a university as the dataset and conducts experimental verification of the model using MATLAB-2023b software under different loads. The experimental results show that the improved neural network model has a fault diagnosis accuracy of 94.27%%, which is 17.84% higher than the traditional neural network model in terms of accuracy. Moreover, for different loads, the improved convolutional neural network model still maintains good fault diagnosis accuracy.