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

Highlights: • Welding process input variables and combination of activating fluxes (SiO₂ and TiO₂) have been modeled and optimized using back propagation neural network and heuristic algorithms in such a way that DOP increased, WBW decreased and desired value ([1-1.4]) for ASR achieved simultaneously. • Back propagation neural network architecture has been determined using PSO algorithm. • Multi-characteristic optimization has been performed via combined BPNN–DF and BPNN-PSO algorithms. • Predicted results are in good agreements (less than 3% error) with those of validation experimental tests. 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.