Abstract:
Artificial Intelligence (AI) plays an increasingly essential role in optimizing
manufacturing processes, where precision and quality are critical. This study
employs the Grey Wolf Optimizer (GWO), a meta-heuristic inspired by grey
wolves, to optimize critical parameters in the submerged arc welding (SAW)
process. The GWO is applied to minimize bead width, a key quality indicator
while balancing influential parameters such as current, voltage, and welding
speed. GWO’s performance is compared to the established Rao method, which
has demonstrated effective results in welding applications. Both methods are
assessed based on varying population sizes and iteration counts to evaluate
parameter convergence across two distinct scenarios. Results indicate that GWO
achieves precise parameter estimation and demonstrates efficiency in complex,
non-linear optimization challenges within the welding process. This research
highlights GWO’s potential as an AI-driven approach to advancing welding
quality and consistency, establishing a foundation for wide applications in
industrial optimization.
