Evolutionary Algorithm On Cold Metal Transfer Process For Feature Extraction

Abstract

In order to achieve the desired bead geometry in a welding operation it is of prime importance to select suitable process parameters. In this research, pulsed MIG welding of 316L austenitic stainless steel is perforated and its bead geometry is studied, such as penetration depth bead width and height of reinforcement. The optimization approach based on the Genetic Algorithm (GA) is implemented to ensure the optimal combination of process variables and bead geometry. Regression model are initially generated by using experimental data. GA is then generated to optimize the parameters of the method and bead geometry parameters by minimizing the objective function based on the least square error. Pulsed MIG welded parameters was experimentally tested by microscopic analysis and EDAX analysis for three sample sets. The finding suggest that expected and experimental values are close in agreement. Finally, the effect of the welding current on the elemental composition is seen. The research shows that in the GA based method, the rate of convergence is faster.