Towards an adaptable quality monitoring process for self-piercing riveting

Abstract

In the automotive industry self-piercing riveting (SPR) is a standard joining technology, especially for vehicles with a high mix of materials. The applied quality control system and the underlying quality decisions have hardly changed in the recent years. The commonly used combination of process monitoring and rework strategies leads to a large number of false positives and a high amount of manual work. The collected process data is not used comprehensively and misses the potential to improve the SPR process. This article introduces a quality monitoring method for SPR to show a proof of concept by using machine learning to predict faulty joining points. Based on numerous technical and statistical features extracted from the force-displacement curve of SPR, the trained model categorizes the observations in two rivet head height (RHH) classes. An evolutionary algorithm is used for the feature selection and a random forest model for classification. The resulting accuracy scores up to 84.4% and shows the potential of the developed random forest model. The potential application of this approach in the context of serial body-shop production improves the prediction of joining quality and the process availability significantly. This enables the adaptable rework of non-critical joining points depending on the classified RHH.