Automatic identification framework of the geometric parameters on self-piercing riveting cross-section using deep learning

Abstract

Self-piercing riveting (SPR) is one of the most important joining technologies used in light-weight vehicle body manufacturing. Traditionally, SPR joints are developed through trial-and-error tests on various rivet and die combinations. Given the hundreds of rivets and dies available in SPR joint design, huge combinations exist in the full solution space. How to optimize the joining process at a low cost with high reliability is critical both for new material, new vehicle and new production line development. Instead of relying on experience-based physical testing, data-driven approach has been believed to be a promising way for future automotive manufacturing and design. However, the lack of effective data acquisition and accurate characterization methods for joints becomes a barrier, which limits the volume and availability of joining data. In present research, an automatic identification framework of the geometric parameters on SPR cross-sections using deep learning is proposed, which integrates an innovated and complete flow from the image pre-process to postprocess. Firstly, cross-section images are transformed into material segmentation maps using deep learning. Then critical control point of a cross-section is identified accurately, and totally 9 key geometrical parameters are measured based on the locations and combinations of control points. The present research shows that SOLOv2 and Unet are the best models for SPR cross-section image segmentation. The proposed framework could provide high accurate measurement results (average error < 0.02 mm) with a very short process time (within secs), laying a solid foundation for data-driven design and optimization of joining processes within the whole design space at vehicle level.