A Study on Predicting Joint Quality Using Self Piercing Riveting Process Signals

This study investigates the prediction of joint quality in self-piercing riveting (SPR) using process signals. As the use of lightweight materials such as aluminum and carbon fiber reinforced polymer (CFRP) increases in the automotive industry, ensuring joint integrity has become critical. Two material combinations were analyzed: CFRP with aluminum vibration damping sheets and galvanized steel (SGACEN) with aluminum vibration damping sheets. Force and Displacement signals were monitored during the SPR process, and their correlation with maximum tensile load was evaluated. The results showed that brittle fracture, occurring vertically during the piercing of CFRP, had a significant impact on joint strength, while the gap between the rivet head and the sheet played a crucial role in determining joint strength in SGACEN-based joints. Various regression models were developed to predict joint quality, and machine learning models such as Randomforest and XGBoost were found to be more suitable than linear regression for use in different material combinations. Both the XGBoost and Randomforest models demonstrated similar accuracy in predicting joint performance, proving their reliability as real-time monitoring solutions for SPR joints.