Semantic weldability prediction with RSW quality dataset and knowledge construction

This paper presents a semantic Resistance Spot Welding (RSW) weldability prediction framework. The framework constructs a shareable weldability knowledge database based on the regression rules from inconsistent RSW quality datasets. This research aims to effectively predict the weldability of RSW process for existing or new weldment design. A real welding test dataset collected from an automotive OEM is used to extract decision rules using a decision tree algorithm, Classification and Regression Trees (CART). The extracted decision rules are converted systematically into SWRL rules for capturing the semantics and to increase the shareability of the constructed knowledge. The experiments show that the RSW ontology, along with SWRL rules that contains weldability rules constructed from the datasets, successfully predicts the weldability (nugget width) values for RSW cases. The predicted nugget width values are found to be in-dose proximity of the actual values. This paper shows that semantic prediction framework construes an intelligent way for constructing accurate and transparent predictive models for RSW weldability verification.