Application of Artificial Intelligence in Welding: Optimizing Process Parameters to Enhance Quality

Abstract

The precision of parameter selection in submerged arc welding (SAW) significantly influences weld quality, strength, and efficiency in industrial manufacturing. Artificial Intelligence (AI) offers advanced tools for tackling the complex, non-linear optimization challenges in welding processes, where traditional trial-and-error methods fall short. This paper introduces the Adolescent Identity Search Algorithm (AISA), an innovative AI-based, human inspired optimization technique, to optimize SAW parameters. Implemented in MATLAB, AISA is applied to minimize bead width—a critical indicator of weld quality—by iteratively refining welding parameters such as current, voltage, and welding speed. To validate AISA’s effectiveness, we conduct a comparative analysis with the Rao algorithm, a recently studied optimization method shown to enhance welding outcomes. We assess both methods under varying population sizes and iteration counts, testing two distinct scenarios to examine their performance and convergence. Results demonstrate AISA’s robustness and adaptability in optimizing welding parameters, offering a viable alternative to existing methods and underscoring the growing importance of AI in advancing manufacturing precision.