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Vision-based sensor for early detection of periodical defects in web materials.

Bulnes FG, Usamentiaga R, García DF, Molleda J - Sensors (Basel) (2012)

Bottom Line: For this reason, it is necessary to have a system that can detect these situations as soon as possible.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 Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Oviedo, Campus de Viesques, Gijón 33204, Spain. bulnes@uniovi.es

ABSTRACT
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.

No MeSH data available.


Steel strip at the finishing mill.
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f9-sensors-12-10788: Steel strip at the finishing mill.

Mentions: In this application, the generation of periodical defects occurs in the finishing mill (Figure 9), where hot steel is stretched and flattened by several rolls, exerting great pressure on steel until it reaches the form of a strip of great length. During the rolling process, the strip has to pass through seven stands. Each one consists of four rolls: two backup rolls and two work rolls. The backup rolls transmit force to the work rolls, which are those in direct contact with the steel. For this reason, the work rolls are those that can generate periodical defects on the surface of the strips. The surface of the work rolls must be completely smooth to obtain a perfect rolling. Besides the typical problems on rolls (cracks or attached objects), in this particular case they become worn very quickly [30], which makes the inspection even more important so as to replace the malfunctioning rolls as early as possible.


Vision-based sensor for early detection of periodical defects in web materials.

Bulnes FG, Usamentiaga R, García DF, Molleda J - Sensors (Basel) (2012)

Steel strip at the finishing mill.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472857&req=5

f9-sensors-12-10788: Steel strip at the finishing mill.
Mentions: In this application, the generation of periodical defects occurs in the finishing mill (Figure 9), where hot steel is stretched and flattened by several rolls, exerting great pressure on steel until it reaches the form of a strip of great length. During the rolling process, the strip has to pass through seven stands. Each one consists of four rolls: two backup rolls and two work rolls. The backup rolls transmit force to the work rolls, which are those in direct contact with the steel. For this reason, the work rolls are those that can generate periodical defects on the surface of the strips. The surface of the work rolls must be completely smooth to obtain a perfect rolling. Besides the typical problems on rolls (cracks or attached objects), in this particular case they become worn very quickly [30], which makes the inspection even more important so as to replace the malfunctioning rolls as early as possible.

Bottom Line: For this reason, it is necessary to have a system that can detect these situations as soon as possible.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 Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Oviedo, Campus de Viesques, Gijón 33204, Spain. bulnes@uniovi.es

ABSTRACT
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.

No MeSH data available.