Optimization research of process parameters for Laser Direct Rapid Forming metal parts based on genetic algorithm

Abstract

The optimization research of process parameters for Laser Direct Rapid Forming (LDRF) metal parts, is a research focus of rapid prototyping technology. The article discussed in detail for solving the optimum process parameters of LDRF technology for the selection approach of strategy of genetic algorithm, the quantitative relationship model was established between process parameters of forming pieces and the part density using neural network method, which process parameters are laser power (P), scanning speed (V), powder feeding rate (G), scan spacing (D) and thickness (ΔZ ) etc., the best configuration program of Genetic Algorithm control parameters has been obtain by means of the parameters encoding, initial group setting, fitness function design, genetic operation design and algorithm control parameters setting. The optimization of process parameters is obtained to fit the LDRF technology by using genetic algorithm toolbox in the MATLAB environment, and the optimization goal of the parts density has also been achieved. Practice has proved that the optimal process parameters are correct by the genetic algorithm, and has a very good production practice guide.