Multi-objective optimization of laser cladding parameters for 42CrMo surface based on GA-BP and NSGA-II algorithms

In order to solve the defects such as pits and microcracks on the surface of 42CrMo bearings, the repair process parameters matched by the high-strength steel of the main bearing were obtained. A three-factor five-level L₂₅(5³) single-layer and single-channel laser cladding orthogonal test was designed to study the optimal combination of process parameters for Ni60A powder cladding on 42CrMo. By using the back propagation (BP) neural network, a model was established with the laser power, powder feeding speed, and scanning speed as the optimization variables. A nonlinear mathematical model with the dilution rate, depth of heat affected zone, and microhardness as the optimization objectives was established, and the model was optimized by a genetic algorithm. Based on the non-dominated sorting genetic algorithm-Ⅱ (NSGA-II), the multi-objective optimization was carried out, and the optimal combination of process parameters was determined from the Pareto solution. The results show that under the conditions of laser power of 1 050 W, powder feeding speed of 0.65 r/min, and scanning speed of 8.8 mm/s, the dilution rate of the cladding layer is 20.00%; the depth of the heat affected zone is 0.650 2 mm, and the microhardness is 603.6 HV. Under the optimal combination of process parameters, the dilution rate decreases by 44.38%; the depth of the heat affected zone reduces by 6.15%, and the microhardness increases by 6.42%. This effectively improves the quality of the cladding layer and achieves the best overall performance.