Simultaneous optimization of joint edge geometry and process parameters in gas metal arc welding using integrated ANN-PSO approach

Gas Metal Arc Welding (GMAW) is one of the most extensively used processes in automated welding due to its high productivity. However, to simultaneously achieve several conflicting objectives such as reducing production time, increasing product quality, full penetration, proper joint edge geometry, and optimal selection of process parameters, a multi-criteria optimization procedure must be used. The aim of this research is to develop a multi-criteria modeling and optimization procedure for GMAW process. To simultaneously predict Weld Bead Geometry (WBG) characteristics and Heat-Affected Zone (HAZ), a Back Propagation Neural Network (BPNN) has been proposed. The experimentally derived data sets are used in training and testing of the network. Results demonstrate that the finely tuned BPNN model can closely simulate actual GMAW process with less than 1% error. Next, to simultaneously optimize process characteristics, the BPNN model is inserted into a Particle Swarm Optimization (PSO) algorithm. The proposed technique determines a set of values for parameters and the workpiece groove angle in such a way that a pre-specified WBG is achieved while the HAZ of the weld joint is minimized. Optimal results are verified through additional experiments.