Data-driven Weld Nugget Width Prediction with Decision Tree Algorithm

Abstract This paper presents the capability of a decision tree algorithm to realize a data-driven resistance spot welding (RSW) weldability prediction. Although RSW provides commendable advantages, such as low cost and high speed/high volume operations, the RSW processes are often inconsistent and these significant inconsistencies are a well-known reliability issue. RSW process and data challenges including inconsistency often hinder the utilization of the data-driven weldability prediction. In this paper, we apply a decision tree algorithm on the RSW dataset collected from an automotive OEM to plot regression trees and to extract decision rules for the weld nugget width prediction. With three RSW test datasets, we conclude that the decision trees help in predicting the nugget width and in determining the impact of design and process parameters to the nugget width response variable.