The structural integrity of self-piercing rivet (SPR) joints is critical in automotive industries, yet its evaluation poses challenges due to the limitations of traditional destructive methods. This research introduces an innovative approach for non-destructive evaluation using micro-CT imaging, Micro-Computed Tomography, combined with machine vision and deep learning techniques, specifically focusing on automated keypoint estimation to assess joint quality. Recognizing the scarcity of real micro-CT data, this study utilizes synthetic data for initial model training, followed by transfer learning to adapt the model for real-world conditions. A UNet-based architecture is employed to localize three keypoints with precision, enabling the measurement of critical parameters such as head height, interlock, and bottom layer thickness. Extensive validation demonstrates that pre-training on synthetic data, complemented by fine-tuning with limited real data, bridges domain gaps and enhances predictive accuracy. The proposed framework not only offers a scalable and cost-efficient solution for evaluating SPR joints but also establishes a foundation for broader applications of machine vision and non-destructive testing in manufacturing processes. By addressing data scarcity and leveraging advanced machine learning techniques, this work represents a significant step toward automated quality control in engineering contexts.
@article{arxiv.2502.16752,
title = {Automated Keypoint Estimation for Self-Piercing Rivet Joints Using micro-CT Imaging and Transfer Learning},
author = {Wei Qin Chuah and Ruwan Tennakoon and Amanda Freis and Mark Easton and Reza Hoseinnezhad and Alireza Bab-Hadiashar},
journal= {arXiv preprint arXiv:2502.16752},
year = {2025}
}