Abstract
The appearance of spot welding reflects the quality of welding to a large extent. In this study, we developed a vision inspection system, which recognizes the defects of weld in electronic components based on neural network. First, the images of weld are acquired by color camera. Then, we extracted 15 features from the welding images that had been corrected and enhanced. Finally, we used 1800 training samples to train the neural network. And then we got a accuracy of 95.82% under 407 testing samples by the neural network classifier, which had 15 input nodes, 4 hidden nodes and 2 output nodes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, Y., Chen, G., Lin, Z.: Study on weld quality control of resistance spot welding using a neuro-fuzzy algorithm. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3215, pp. 544–550. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30134-9_73
Khodabakhshi, F., Kazeminezhad, M., Kokabi, A.H.: Metallurgical characteristics and failure mode transition for dissimilar resistance spot welds between ultra-fine grained and coarse-grained low carbon steel sheets. Mater. Sci. Eng., A 637, 12–22 (2015)
Khodabakhshi, F., Kazeminezhad, M., Kokabi, A.H.: On the failure behavior of highly cold worked low carbon steel resistance spot welds. Metall. Mater. Trans. A 45, 1376–1389 (2014)
Cho, Y., Rhee, S.: New technology for measuring dynamic resistance and estimating strength in resistance spot welding. Meas. Sci. Technol. 11, 1173–1178 (2000)
Chen, Z.H., Shi, Y.W., Jiao, B.Q., Zhao, H.Y.: Ultrasonic nondestructive evaluation of spot welds for bzinc-coated high strength steel sheet based on wavelet packet analysis. J. Mater. Process. Technol. 209, 2329–2337 (2009)
Liu, J., Xu, G.C., Xu, D.S., Zhou, G.H., Fan, Q.Y.: Ultrasonic C-scan detection for stainless steel spot welding based on wavelet package analysis. J. Wuhan Univ. Technol. 30, 580–585 (2015)
Ruisz, J., Biber, J., Loipetsberger, M.: Quality evaluation in resistance spot welding by analyzing the weld fingerprint on metal bands by computer vision. Int. J. Adv. Manuf. Technol. 33, 952–960 (2007)
Wang, Y., Sun, Y., Lv, P., Wang, H.: Detection of line weld defects based on multiple thresholds and support vector machine. NDT&E Int. 41, 517–524 (2008)
Zhang, X.G., Xu, J.J., Li, Y.: The research of defect recognition for radiographic weld image based on fuzzy neural network. In: Fifth World Congress on Intelligent Control and Automation, WCICA 2004 (2004)
Zhang, A.H., Yu, S.S., Zhou, J.L.: A local-threshold segment algorithm based on edge-detection. Mini-micro Systems 2003-04 (2003)
Neural Network: What is a Neural Network. http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#
Martín, Ó., López, M., Martín, F.: Artificial neural networks for quality control by ultrasonic testing in resistance spot welding. J. Mater. Process. Technol. 183, 226–233 (2006)
Acknowledgement
This work has been supported in part by the National Natural Science Foundation of China (21373173).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ye, S., Guo, Z., Zheng, P., Wang, L., Lin, C. (2017). A Vision Inspection System for the Defects of Resistance Spot Welding Based on Neural Network. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-68345-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68344-7
Online ISBN: 978-3-319-68345-4
eBook Packages: Computer ScienceComputer Science (R0)