An efficient method for defect detection during the manufacturing of web materials
- 285 Downloads
Defect detection is becoming an increasingly important task during the manufacturing process. The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the economic impact caused by discarding defective products. This point is especially important in the case of products that are very expensive to produce. In this paper, the authors propose a method to detect a specific type of defect that may occur during the production of web materials: periodical defects. This type of defect is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale. To run the proposed method, two different functions must be executed a large number of times. Since the time available to perform the detection of these defects may be limited, it is very important to consume the least amount of time possible. In order to reduce the overall time required for detection, an analysis of how the method accesses the input data is performed. Thus, the most efficient data structure to store the information is determined. At the end of the paper, several experiments are performed to verify that both the proposed method and the data structure used to store the information are the most suitable to solve the aforementioned problem.
KeywordsPeriodical defects Quality control Inspection Data structures
The authors would like to thank the technicians and the engineers of ArcelorMittal Aviles for their helpful cooperation. This work was supported by contracts with ArcelorMittal corporation.
- Agarwal, K., & Shivpuri, R. (2013). Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0730-5.
- Bernie, J., & Trepanier, R. (2009). A new technique for measuring periodic patterns within a paper sheet. Pulp and Paper Canada, 110(6), 39–42.Google Scholar
- Duan, X., Duan, F., & Han, F. (2011). Study on surface defect vision detection system for steel plate based on virtual instrument technology. In 2011 International conference on control, automation and systems engineering, CASE 2011. Article Id. 5997625. Google Scholar
- Gutemberg, G. F. (2009). Disambiguating the recognition of 3d objects. In 2009 IEEE computer society conference on computer vision and pattern recognition workshops, CVPR workshops 2009 (pp. 2278–2285).Google Scholar
- Kumar, A. (2008). Computer-vision-based fabric defect detection: A survey. IEEE Transactions on Industrial Electronics, 55(1), 348–363.Google Scholar
- Mian, A. S., Bennamoun, M., & Owens, R. A. (2004). A novel algorithm for automatic 3d model-based free-form object recognition. In Conference proceedings—IEEE international conference on systems, man and cybernetics (Vol. 7, pp. 6348–6353).Google Scholar
- Park, C., Choi, S., & Won, S. (2010). Vision-based inspection for periodic defects in steel wire rod production. Optical Engineering, 49(1), 017202-1–017202-10.Google Scholar
- Shigeno, M., Yamauchi, K., & Amanuma, Y. (2007). Development of surface detector for no. 3 tin temper mill (3tmp). JFE Technical Report, 9, 64–69.Google Scholar
- Tewarson, R. P. (1973). Sparse matrices. New York: Academic Press Inc.Google Scholar
- Traxler, G., Klarner, J., & Huelble-Koenigsberger, G. (2005). Broken roll detection, application, algorithm and its basic principles of sensing. In Proceedings of SPIE—the international society for optical engineering (Vol. 5679, pp. 146–155).Google Scholar
- Wan, W. H. (2001). Product quality control and its applications in industry. Journal of System Simulation, 13, 154–155.Google Scholar
- Wang, F., Xu, X., Yuan, C., & Beek, P. V. (2011). Supervised and semi-supervised online boosting tree for industrial machine vision application. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (pp. 43–51).Google Scholar