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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.


Defect generation due to a defective roll.
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f1-sensors-12-10788: Defect generation due to a defective roll.

Mentions: Quality control in industrial environments is an essential aspect, both to improve product quality and to reduce costs caused by discarded defective products. The current trend is to replace human experts for automated systems, since they are cheaper, take less time and can work even in the most dangerous environments [1]. This study focuses on quality control during production of web materials using a computer vision system. Such materials can be defined as those which are produced by continuous rolling processes, such as paper, plastic, fabric or metal. The rolls that are in direct contact with the web material to be produced can be the source of serious defects. When their surfaces have some kind of flaw, they can generate a surface defect on the rolled product each time they complete a full turn, generating a periodical pattern as shown in Figure 1. In these cases, this situation is repeated until the defective roll is replaced. For this reason, it is very important to make early detection of such defects during the production of web materials. The sooner this situation is detected, the sooner the defective roll may be replaced, greatly reducing the productivity loss.


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

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

Defect generation due to a defective roll.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-10788: Defect generation due to a defective roll.
Mentions: Quality control in industrial environments is an essential aspect, both to improve product quality and to reduce costs caused by discarded defective products. The current trend is to replace human experts for automated systems, since they are cheaper, take less time and can work even in the most dangerous environments [1]. This study focuses on quality control during production of web materials using a computer vision system. Such materials can be defined as those which are produced by continuous rolling processes, such as paper, plastic, fabric or metal. The rolls that are in direct contact with the web material to be produced can be the source of serious defects. When their surfaces have some kind of flaw, they can generate a surface defect on the rolled product each time they complete a full turn, generating a periodical pattern as shown in Figure 1. In these cases, this situation is repeated until the defective roll is replaced. For this reason, it is very important to make early detection of such defects during the production of web materials. The sooner this situation is detected, the sooner the defective roll may be replaced, greatly reducing the productivity loss.

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.