Optimization of ATIG Weld Based on a Swarm Intelligence Approach: Application to the Design of Welding in Selected Manufacturing Processes

Tungsten Inert Gas (TIG) welding is a widespread welding process used in the industry for high-quality joints. However, this welding process suffers from lower productivity. Activated Tungsten Inert Gas (ATIG) is a variant of the TIG that aims to increase the depth penetration capability of conventional TIG welding. This is achieved by applying a thin coating of activating flux material onto the workpiece surface before welding. This work investigates the effect of the thermophysical properties of individual metallic oxide fluxes on 316L stainless steel weld morphology. Four levels of current intensity (120, 150, 180, 200 A) are considered. The weld speed up to 15 cm/min and arc length of 2 mm are maintained constant. Thirteen oxides were tested under various levels of current intensity in addition to multiple thermophysical properties combinations, and the depth penetration (D) and the aspect ratio (R) were recorded. This process has provided 52 combinations (13 oxides * 4 currents). Based on the numerical observations, linear and nonlinear models for describing the effect of the thermophysical parameters on the weld characteristics were tuned using a particle swarm optimization algorithm. While the linear model provided good prediction accuracy, the nonlinear exponential model outperformed the linear one for efficiency based on algorithmization of the planning process.