Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm

Abstract

Flux cored arc welding (FCAW) process is a fusion welding process in which the welding electrode is a tubular wire that is continuously fed to the weld area. It is widely used in industries and shipyards for welding heavy plates. Welding input parameters play a very significant role in determining the quality of a weld joint. This paper addresses the simulation of weld bead geometry in FCAW process using artificial neural networks (ANN) and optimization of process parameters using particle swarm optimization (PSO) algorithm. The input process variables considered here include wire feed rate (F); voltage (V); welding speed (S) and torch Angle (A) each having 5 levels. The process output characteristics are weld bead width, reinforcement and depth of penetration. As per the statistical design of experiments by Taguchi L25 orthogonal array, bead on plate weldments were made. The experimental results were fed to the ANN algorithm for establishing a relationship between the input and output parameters. The results were then embedded into the PSO algorithm which optimizes the process parameters subjected to the objectives. In this study the objectives considered are maximization of depth of penetration, minimization of bead width and minimization of reinforcement.