Machine Learning Approach for Corrosion Analysis in Copper–Stainless Steel Welds Produced by Cold Gas Tungsten Arc Welding

ABSTRACT

The corrosion behavior of galvanically coupled copper–AISI 304 stainless‐steel welds (Cu‐Fe immiscible system) fabricated using the novel cold gas tungsten arc welding (CGTAW) process was investigated. The controlling factors and mechanisms governing corrosion in three types of anode‐dominated weld microstructures were discussed. In a contaminated circulating water environment, corrosion rates were associated with the degree of mixing between the Cu‐matrix and Fe‐based phases (uniform or over‐mixed distributions) and with the presence of defects. A hierarchical Random Forest (RF)‐based machine learning framework was developed to model the process–structure–property relationship, in which process parameters influence corrosion rate through intermediate microstructural evolution and defect formation.