Multi-Objective Optimization of Laser Cladding Parameters Based on BP Neural Network

In order to obtain the optimal process parameters for laser melting of TiC iron-based alloy powder on 316L stainless steel, a back propagation (BP) neural network based on genetic algorithm optimization for laser melting parameters optimization is proposed. A three-factor, five-level full factorial experiment was designed to measure the macroscopic morphology and average hardness of the melted layer, and a neural network model was established for the input parameters (laser power, scanning speed, and protective gas flow rate) and response quantities (melted layer width, melted layer height, dilution rate, and microhardness). The effect of the process parameters on the response quantity was analyzed by multiple non-linear regression, and the overall performance of the clad layer was characterized by the integrated gray correlation, and the optimal parameters were obtained. The experimental results show that the laser power and scanning speed have obvious effects on the width of the molten layer, dilution rate and microhardness, while the protective gas flow rate has the most significant effect on the height of the molten layer. The goodness of fit of each response quantity model of the BP neural network model optimized by the genetic algorithm reaches between 0. 85 and 0. 91, and the GA-BP model has good accuracy. The best overall performance was achieved when the parameter was 1090 W, the scanning speed was 4. 4 mm/s, and the protective gas flow rate was 10 L/min, indicating that the BP neural network algorithm was suitable for the quality control and parameter optimization of the laser cladding layer.