Computer vision for automatic defect detection of self-pierce rivet joints

Abstract

Rising costs for raw materials and energy require the development of lightweight construction concepts that are suitable for the load. Especially in the transportation sector, lightweight construction of structural components leads to improvements in resource- and energy-efficiency. The joining process of self-pierce riveting with semi-hollow pierce rivet is often used to join these lightweight materials. To ensure the product properties, the quality of each joint must be guaranteed. Currently, checking the joint quality is done manually through cyclical visual inspections and destructive tests, which costs considerable resources and time. Hence, a timely defect detection during the production process, based on computer vision, can save costs and enables better quality documentation of the rivet joints.In this paper a computer vision approach, based on the object detection algorithm YOLOv5, for automated defect detection of self-pierce riveting joints is presented. To this end, current methods for quality control of rivet joints in automotive production are discussed, as well as existing research on computer vision for defect detection on self-pierce rivet joints. Images from rivet joints on two different sheet metal pairings have been gathered and labelled, containing five different defect classes that can be found in automotive production. The images comprise a 90 ° and 45 ° viewing angle of the rivet joints to compare the algorithm's performance for these angles. To estimate the influence of training data samples on the defect detection performance the algorithm was trained and tested on different training data sizes. Furthermore, the transferability of the learned model from an aluminium sheet metal to an aluminium die cast pairing is assessed. It has been found that computer vision delivers sound results at the task of detecting defects in rivet joints, even with small training data size and hence has the potential to improve quality control in automotive riveting processes.