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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.16752 |
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| _version_ | 1866910842098810880 |
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| author | Chuah, Wei Qin Tennakoon, Ruwan Freis, Amanda Easton, Mark Hoseinnezhad, Reza Bab-Hadiashar, Alireza |
| author_facet | Chuah, Wei Qin Tennakoon, Ruwan Freis, Amanda Easton, Mark Hoseinnezhad, Reza Bab-Hadiashar, Alireza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Keypoint Estimation for Self-Piercing Rivet Joints Using micro-CT Imaging and Transfer Learning Chuah, Wei Qin Tennakoon, Ruwan Freis, Amanda Easton, Mark Hoseinnezhad, Reza Bab-Hadiashar, Alireza Computational Engineering, Finance, and Science 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. |
| title | Automated Keypoint Estimation for Self-Piercing Rivet Joints Using micro-CT Imaging and Transfer Learning |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2502.16752 |