Automatic identification framework of the geometric parameters on self-piercing riveting cross-section using deep learning. (November 2022)

• Record Type: Journal Article
• Authors: Li, Mushi; Liu, Zhao; Huang, Li; Chen, Qiuren; Tong, Chao; Fang, Yudong; Han, Weijian; Zhu, Ping
• Is Part Of: Journal of manufacturing processes. Volume 83(2022)
• Page Start: 427
• Page End: 437
• Publication Date: 2022-11
• Subjects: Self-piercing riveting -- Key geometric parameters -- Automatic identification -- Image segmentation -- Deep learning; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.09.020
• Languages: English
• ISSNs: 1526-6125

Abstract

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. Highlights: To the author's knowledge, this is the first successful application of deep learning to the identification of the geometrical parameters on self-piercing riveting. To maximize the effectiveness of the deep learning model, the performance of four representative deep learning image segmentation models is tested. The test results show that SOLOv2 and Unet are the most suitable deep neural network models for SPR cross-section image segmentation whose mIoU can exceed 0.975. The time for this framework to identity an image was less than 10s with a regular hardware setup, which reduced the measurement time by >95 % compared with manual measurement. The average errors of the 9 key parameters automatically identified and manually measured were within 0.02 mm, that is, 4 pixels.