Laser cutting quality prediction based on pareto genetic algorithm

Abstract

Prediction and optimization of cutting quality is an important method to improve the cutting quality. Aiming at the prediction of quality characteristic parameters for pulsed Nd: YAG laser cutting, a prediction algorithm based on pareto genetic algorithm is used in this paper. KW (Kerf Width) and MRR (Material removal rate) are selected as the optimization objective, and the multi-objective optimization model is established in this paper. The theoretical analysis and experimental results show that the algorithm can be used for KW and MRR prediction in pulsed Nd: YAG laser cutting. A large number of forecast data show the rules as follows. The effects of three types of combined parameters (gas pressure and pulse width, pulse width and pulse frequency, pulse width and cutting speed) on KW are obvious, while the effects of combined parameters, pulse width and pulse frequency, pulse frequency and cutting speed are more obvious on MRR. The study in this paper can provide theoretical guidance and parameters for prediction and optimization of quality in laser cutting.