Modeling and optimization of A-GTAW process using back propagation neural network and heuristic algorithms

Abstract

Apart from different merits of using conventional gas tungsten arc welding (C-GTAW) process, some demerits have been introduced among which shallow penetration is the most important ones. In order to cope with the mentioned disadvantage, some procedures have been proposed among which using a paste like coating of activating flux during welding process known as activated-GTAW (A-GTAW) is the most extensively used ones. 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). Dragonfly (DFA) 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 3% error) of A-GTAW process.