Modeling and optimization of integrated flux assisted-welding process using a hybrid ANN-SA approach (A case study in Rumaila combined cycle power plant, Basra, Iraq)

In this study an artificial neural network (ANN) based modeling and a heuristic based optimization procedure using simulated annealing (SA) algorithm for modeling and optimization of flux assisted TIG welding process known as activated TIG (A-TIG) have been addressed. In this study effect of the most important process variables (welding current (C), welding speed (S)) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on the most important quality characteristics (depth of penetration (DOP), weld bead width (WBW), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been considered. To gather the required data for modeling and optimization purposes, box-behnken design (BBD) in design of experiments (DOE) approach has been used. In order to establish a relation between process input variables and output characteristics, back propagation neural network (BPNN) has been employed results of which have been compared with regression modeling outputs. Particle swarm optimization (PSO) algorithm has been used for determination of BPNN architecture (number of hidden layers and neurons/nodes in each hidden layer). Simulated annealing (SA) and PSO algorithms have been employed for process optimization in such a way that desired AR, minimum WBW, and maximum DOP achieved simultaneously. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Based on the results, the proposed procedure is efficient in modeling and optimization (with less than 4% error) of A-GTAW process.