Development of Machine Learning Models to Predict the Weld Defect Using Resistance Spot Welding Experimental Data

Abstract

Advanced materials and automated processes in manufacturing pose a challenge in terms of adaptability. Introduction of 3rd-generation advanced high strength (3rd-gen AHSS) steels aimed for weight reduction in the automotive without compromising its strength and efficient fuel consumption. Nevertheless, welding 3rd-gen AHSS using resistance spot welding (RSW) is often affected by liquid metal embrittlement (LME) or other quality matters. Identifying the process window to control and produce defect-free welds, requires huge experimental work with an enormous time. Therefore, this paper aimed to use machine learning (ML) to identify the process window by computing the relationship between the input parameters and output weld defect categories like 'Splash', 'LME', 'Insufficient nugget size' and 'Good weld'. Classification-based algorithms, K-nearest-neighbour (KNN) and Naive Bayes algorithms were used. Among these, Naive Bayes exhibits better prediction efficiency of 71% and KNN has 63%. Using these models, predicting the incidence of weld spot defects with the fore-mentioned predictability is possible. Therefore, this work supports the industry experts and researchers to study and predict the process window for the welding process to produce defect-free welds and this idea could implement in different manufacturing processes.