Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach

Abstract

Gas tungsten arc welding (GTAW) is the most extensively used process capable of fabricating a wide range of alloys based on its distinctive merits has been introduced. However, some demerits have been reported among which shallow penetration is the most crucial ones. In order to deal with the poor penetration of the process, different procedures have been proposed among which activated gas tungsten arc welding (A-GTAW) is the most extensively used one. In this study effect of percentage of activating fluxes (TiO2 and SiO2) combination (F) and the most important process variables (welding current (C), welding speed (S)) on the most important process measures (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been investigated. Box-behnken design (BBD) has been used to design the experimental matrix required for date gathering, modeling, and statistical analysis purposes. A neural network with a back propagation algorithm (BPNN) in artificial neural network (ANN) modeling approach has been employed to relate the process input variables and output characteristics. The proper BPNN architecture (number of hidden layers and neurons/nodes in each hidden layer) has been determined using particle swarm optimization (PSO) algorithm. Moreover, process optimization in such a way that maximum DOP, minimum WBW, and desired ASR achieved has been carried out using PSO algorithm. Next, the performance of PSO algorithm has been checked using simulated annealing (SA) algorithm. Finally, to evaluate the performance of the proposed method a set of confirmation experimental test has been conducted. Results of experimental tests revealed that the proposed method is quite efficient in modeling and optimization (with less than 4% error) of A-GTAW process.