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A Crack Detection Method for Self-Piercing Riveting Button Images through Machine Learning
Technical Paper
2020-01-0221
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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.
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Citation
Jiang, 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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References
- Han , L. , Thornton , M. , and Shergold , M. A Comparison of the Mechanical Behaviour of Self-Piercing Riveted and Resistance Spot Welded Aluminium Sheets for the Automotive Industry Materials & Design 31 3 1457 1467 2010 10.1016/j.matdes.2009.08.031
- Shu-Min , W. , Shi-Xin , H.U. , Lian-Hong , Z. , Shuang-Yi , L.I. et al. Affections of Mould and Die Technological Parameters on Self-Piercing Riveting Technical Process and Riveted Joint Quality Journal of Machine Design 04 62 65 2008 10.13841/j.cnki.jxsj.2008.04.005
- Guan , H. et al. Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data IEEE Transactions on Geoscience and Remote Sensing 53 3 1527 1537 2015 10.1109/TGRS.2014.2344714
- Johnson , P. , Cullen , J.D. , Sharples , L. et al. Online Visual Measurement of Self-Pierce Riveting Systems to Help Determine the Quality of the Mechanical Interlock Measurement 42 5 661 667 2009 10.1016/j.measurement.2008.10.013
- Bhuiyan , M.Y. , Shen , Y. , and Giurgiutiu , V. Guided Wave Based Crack Detection in the Rivet Hole Using Global Analytical with Local FEM Approach Materials 9 7 602 2016 10.3390/ma9070602
- Hoang , N.D. and Nguyen , Q.L. A Novel Method for Asphalt Pavement Crack Classification Based on Image Processing and Machine Learning Engineering with Computers 2018 10.1007/s00366-018-0611-9
- Abdelqader , I. , Abudayyeh , O. , and Kelly , M.E. Analysis of edge-detection techniques for crack identification in bridges Journal of Computing in Civil Engineering 17 4 255 2003 10.1061/(ASCE)0887-3801
- Ayenu-Prah , A. and Attoh-Okine , N. Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition Eurasip Journal on Advances in Signal Processing 1 1 1 7 2008 2008 10.1155/2008/861701
- Oliveira , H. and Correia , P.L. Automatic Road Crack Detection and Characterization IEEE Transactions on Intelligent Transportation Systems 14 1 155 168 2013 10.1109/TITS.2012.2208630
- Zhigang , X. et al. Asphalt Pavement Crack Recognition Algorithm Based on Histogram Estimation and Shape Analysis Chinese Journal of Scientific Instrument 31 10 2260 2266 2010 10.19650/j.cnki.cjsi.2010.10.017
- Chen , F. et al. A Texture-Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces Computer-Aided Civil and Infrastructure Engineering 32 4 271 287 2017 10.1111/mice.12256
- Kaseko , M.S. and Ritchie , S.G. A Neural Network-Based Methodology for Pavement Crack Detection and Classification Transportation Research Part C Emerging Technologies 1 4 275 291 1993 10.1016/0968-090X(93)90002-W
- Xu , G. et al. Automatic Recognition of Pavement Surface Crack Based on BP Neural Network International Conference on Computer & Electrical Engineering 2009 10.1109/ICCEE.2008.96
- Rezwanul , H. Quality of Self-Piercing Riveting (SPR) Joints from Cross-Sectional Perspective: A Review Archives of Civil and Mechanical Engineering 18 1 83 93 2018 10.1016/j.acme.2017.06.003
- Solomon , C. and Breckon , T. Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab Prentice Hall 2011 10.1007/978-981-10-7272-7_1
- Elad , M. On the Origin of the Bilateral Filter and Ways to Improve It IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 11 10 2002 10.1109/tip.2002.801126
- Haralick , R.M. , Shanmugam , K. , and Dinstein , I. Textural Features for Image Classification Systems Man & Cybernetics IEEE Transactions 3 6 610 621 1973 10.1109/TSMC.1973.4309314
- Preethi , G. and Sornagopal , V. MRI Image Classification Using GLCM Texture Features International Conference on Green Computing Communication & Electrical Engineering IEEE 2014 10.1109/ICGCCEE.2014.6922461
- Baek , J. et al. Efficient Pedestrian Detection at Nighttime Using a Thermal Camera Sensors 17 8 1850 2017 10.3390/s17081850
- Tajeripour , F. and Kabir , E. Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns International Conference on Conference on Computational Intelligence & Multimedia Applications IEEE 2008 10.1109/ICCIMA.2007.50
- Ojala , T. , Pietikäinen , M. , and Mäenpää , T. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns IEEE Transactions on Pattern Analysis & Machine Intelligence 24 7 971 987 2002 10.1109/tpami.2002.1017623