Optimization of Laser Additive Manufacturing Process Based on XGBoost Algorithm

Laser cladding is a widely used additive manufacturing technology, which has significant practical significance for the repair of steel parts. However, traditional laser additive technology is not efficient in predicting the morphology of steel and analyzing data. Therefore, to improve the manufacturing and repair efficiency of laser additive processes, this study uses extreme gradient enhancement algorithms and point cloud data to optimize traditional processes. The research results showed that the optimized process improved efficiency by 4% in a model with a 20 tilt angle substrate, 3% after optimization with a 30 tilt angle, and 2% after optimization with a 40 tilt angle. Therefore, the use of extreme gradient enhancement algorithms and point cloud data optimized process flow can effectively improve the manufacturing and repair efficiency of laser additive materials. This provides new ideas for future research in this direction.