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.

除了使用常规的气体保护钨极电弧焊（C-GTAW）方法的不同的优点，一些缺点已其中浅渗透是最重要的引入。为了应付上述缺点，一些程序已经提出了其中使用诸如焊接称为活化-GTAW（A-GTAW）过程中活性剂的涂覆糊是最广泛使用的。在最重要的过程变量（焊接电流（C），焊接速度（S））和激活通量百分比（二氧化钛的本研究效果₂和SiO ₂）组合（F）上的最重要的质量特性（渗透（DOP的深度），焊道宽度（WBW），并在AISI316L的焊接奥氏体不锈钢零件因此纵横比（ASR））已经被考虑。要收集建模和优化的目的所需的数据，美国能源部（DOE）的方法已被用来在实验设计盒Behnken法设计（BBD）。为了建立过程输入变量和输出特性之间的关系，反向传播神经网络（BPNN）已经采用其中已与回归建模输出比较结果。粒子群优化（PSO）算法已被用于测定BPNN架构（隐藏层的神经元和/每个隐藏层结点的数目）。蜻蜓（DFA）和PSO算法已被用于工艺优化在期望的AR，最小WBW这样的方式，和最大​​DOP同时实现。最后，确认实验测试已经进行了评估该方法的性能。基于这些结果，所提出的过程是在建模和A-GTAW方法的优化（具有小于3％的误差）高效。