Role of AI in Optimization of MIG Welding Parameters

Mechanical Engineering

Metal Inert Gas (MIG) welding, also called Gas Metal Arc Welding (GMAW), depends on process parameters such as current, voltage, wire feed speed, travel speed, and gas flow rate. Small variations in these settings can change bead geometry, penetration, mechanical properties, and defect formation. Traditionally, parameter selection relies on operator experience or design of experiments, which can be time-consuming and sub-optimal. Artificial Intelligence (AI) techniques provide an alternative by using machine learning and optimization algorithms to predict weld quality, detect defects, and automatically search for the best parameter combinations. This paper reviews AI applications in MIG welding, presents a structured workflow for parameter optimization, and demonstrates how Pareto optimization can balance trade-offs between penetration and spatter.

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