During the production of web materials such as plastic, textiles or metal, where there are rolls involved in the production process, periodically generated defects may occur. If one of these rolls has some kind of flaw, it can generate a defect on the material surface each time it completes a full turn. This can cause the generation of a large number of surface defects, greatly degrading the product quality. For this reason, it is necessary to have a system that can detect these situations as soon as possible. This paper presents a vision-based sensor for the early detection of this kind of defects. It can be adapted to be used in the inspection of any web material, even when the input data are very noisy. To assess its performance, the sensor system was used to detect periodical defects in hot steel strips. A total of 36 strips produced in ArcelorMittal Avilés factory were used for this purpose, 18 to determine the optimal configuration of the proposed sensor using a full-factorial experimental design and the other 18 to verify the validity of the results. Next, they were compared with those provided by a commercial system used worldwide, showing a clear improvement.
References
[1]
Malamas, E.N.; Petrakis, E.G.M.; Zervakis, M.; Petit, L.; Legat, J.D. A survey on industrial vision systems, applications and tools. Image Vis. Comput. 2003, 21, 171–188, doi:10.1016/S0262-8856(02)00152-X.
[2]
Zhang, Z.F.; Gao, Z.; Liu, Y.Y.; Jiang, F.C.; Yang, Y.L.; Ren, Y.F.; Yang, H.J.; Yang, K.; Zhang, X.D. Computer vision based method and system for online measurement of geometric parameters of train wheel sets. Sensors 2012, 12, 334–346. 22368472
[3]
Adamo, F.; Attivissimo, F.; Nisio, A.D. Calibration of an inspection system for online quality control of satin glass. IEEE Trans. Instrum. Meas. 2010, 59, 1035–1046, doi:10.1109/TIM.2010.2040963.
[4]
Huang, W.; Kovacevic, R. A laser-based vision system for weld quality inspection. Sensors 2011, 11, 506–521, doi:10.3390/s110100506. 22344308
Navarro, P.; Iborra, A.; Fernandez, C.; Sanchez, P.; Suardiaz, J. A sensor system for detection of hull surface defects. Sensors 2010, 10, 7067–7081, doi:10.3390/s100807067. 22163590
[7]
Telljohann, A. Chapter 2, Introduction to Building a Machine Vision Inspection. In Handbook of Machine Vision; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2007; pp. 35–71.
[8]
Brzakovic, D.; Vujovic, N. Designing a defect classification system: A case study. Pattern Recog. 1996, 29, 1401–1419, doi:10.1016/0031-3203(95)00166-2.
[9]
Chang, C.A.; Su, C.T. A comparison of statistical regression and neural network methods in modeling measurement errors for computer vision inspection systems. Comput. Ind. Eng. 1995, 28, 593–603, doi:10.1016/0360-8352(94)00211-5.
[10]
Bulnes, F.G.; Garcia, D.F.; Usamentiaga, R.; Molleda, J. A Technique for Clustering Individual Defects from Images of Steel Strips in Periodical Defects. Proceedings of the 12th IAPR Conference on Machine Vision Applications, Nara, Japan, June 2011; pp. 283–286.
[11]
Bulnes, F.G.; Usamentiaga, R.; Garcia, D.F.; Molleda, J.; Rendueles, J.L. Vision-based technique for periodical defect detection in hot steel strips. IEEE Ind. Appl. Mag. 2012. in press.
[12]
Bulnes, F.G.; Usamentiaga, R.; Garcia, D.F.; Molleda, J. Detection of Periodical Patterns in the Defects Identified by Computer Vision Systems. Proceedings of the IEEE 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain, November 2011. Volume 1; pp. 301–306.
[13]
Bissessur, Y.; Martin, E.; Morris, A. Monitoring the performance of the paper making process. Control Eng. Pr. 1999, 7, 1357–1368, doi:10.1016/S0967-0661(99)00105-7.
[14]
I'Anson, S. Identification of periodic marks in paper and board by image analysis using two-dimensional fast Fourier transforms. Tappi J. 1995, 78, 113–119.
[15]
Bernie, J.P.; Trepanier, R. A New Technique to Characterize Periodic and Pseudo-Periodic Marks Within a Sheet of Paper. Proceedings of the 2007 TAPPI Papermakers and PIMA International Leadership Conference, Jacksonville, FL, USA, March 2007.
[16]
Bernie, J.; Trepanier, R. A new technique for measuring periodic patterns within a paper sheet. Pulp Pap. Can. 2009, 110, 39–42.
[17]
Chaplin, D. Image analysis: A powerful tool to pinpoint defects and gauge print quality. Pap. Age 1998, 114, 30–31.
[18]
Kumar, A. Computer-vision-based fabric defect detection: A survey. IEEE Trans. Ind. Electron. 2008, 55, 348–363, doi:10.1109/TIE.1930.896476.
[19]
Toba, E. Determination of autocorrelation function of woven fabrics by using laser speckle. Text. Res. J. 1980, 50, 238–244, doi:10.1177/004051758005000407.
[20]
Drean, J.Y.; Harzallah, O.; Renner, M. Textile surfaces analysis and modeling based on statistical mmethods: Variance analysis and autocorrelation functions. Text. Res. J. 2010, 80, 1833–1845, doi:10.1177/0040517510371867.
[21]
Kianiha, A.; Sheikhzadeh, M.; Semnani, D.; Dabirian, F. An investigation into the weft insertion periodic errors in fabric using an image analysis method. J. Text. Inst. 2010, 101, 457–462, doi:10.1080/00405000802517095.
[22]
Duan, X.; Duan, F.; Han, F. Study on Surface Defect Vision Detection System for Steel Plate Based on Virtual Instrument Technology. Proceedings of the 2011 International Conference on Control, Automation and Systems Engineering CASE 2011, Singapore, Singapore, July 2011.
[23]
Lee, K.S.; Kim, K. Surface quality improvement by SDD in POSCO. SEAISI Q. 2003, 32, 58–58.
[24]
Shigeno, M.; Yamauchi, K.; Amanuma, Y. Development of Surface Detector for No. 3 Tin Temper Mill (3TMP); JFE Technical Report; JFE Holdings Inc.: Tokyo, Japan, 2007; pp. 64–69.
[25]
Traxler, G.; Klarner, J.; Huelble-Koenigsberger, G. Broken roll detection, application, algorithm and its basic principles of sensing. Proc. SPIE 2005, 5679, 146–155.
[26]
Park, C.; Choi, S.; Won, S. Vision-based inspection for periodic defects in steel wire rod production. Opt. Eng. 2010, 49, doi:10.1117/1.3284779.
[27]
Oerley, H.; Bauereiss, U.; Strobl, F. In-Line Inspection of Base Film and Its Coating. Proceedings of the TAPPI European PLACE Conference 2, Budapest, Hungary, May 2009. Volume 2; pp. 1610–1649.
[28]
Caron, J.; Duvieubourg, L.; Orteu, J.; Revolte, P. Real-time inspection of preweathered Zinc based on computer vision. Proc. SPIE 1996, 2665, 270–281.
[29]
Van Rijsbergen, C.J. Information Retrieval, 2nd ed. ed.; Butterworths: Oxford, United Kingdom, 1979.
[30]
Cowling, P. A flexible decision support system for steel hot rolling mill scheduling. Comput. Ind. Eng. 2003, 45, 307–321, doi:10.1016/S0360-8352(03)00038-X.
[31]
Zhao, S.; Huang, H.; Wang, F.; Li, Y.; Wei, G. Estimating periodic length and defect coordinates from a series of integer values. Meas. J. Int. Meas. Confed. 2009, 42, 516–523, doi:10.1016/j.measurement.2008.09.005.
[32]
Jeong, J.H.; Kalowitsch, M. State-of-the-art surface inspection technology with parsytec 5i. SEAISI Q. 2007, 36, 10–15.
