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A Crack Detection Method for Self-Piercing Riveting Button Images through Machine Learning
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images. The preliminary results on non-cracked and cracked button images show that the proposed crack detection method is an effective approach to identify a potential defect.
CitationJiang, L., Huang, L., Wang, X., Zhan, Z. et al., "A Crack Detection Method for Self-Piercing Riveting Button Images through Machine Learning," SAE Technical Paper 2020-01-0221, 2020, https://doi.org/10.4271/2020-01-0221.
Data Sets - Support Documents
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