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Sensors 2012, 12(8), 10788-10809; doi:10.3390/s120810788

Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials

Department of Computer Science, University of Oviedo, Campus de Viesques, Gijón 33204, Spain
Author to whom correspondence should be addressed.
Received: 21 May 2012 / Revised: 25 July 2012 / Accepted: 25 July 2012 / Published: 6 August 2012
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: vision sensors; intelligent systems; automated defect detection; pattern recognition vision sensors; intelligent systems; automated defect detection; pattern recognition

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Bulnes, F.G.; Usamentiaga, R.; García, D.F.; Molleda, J. Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials. Sensors 2012, 12, 10788-10809.

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