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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.


Steps of the clustering method.
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f6-sensors-12-10788: Steps of the clustering method.

Mentions: Figure 6 shows the main steps of the clustering method. Accordingly, once the initial solution has been created and the clustering algorithm has been executed, periodical defects placed at other transversal positions are sought. To do this, the histogram is calculated again, without taking into account the individual defects included in a periodical defect previously detected. An initial solution is created for that transversal position and the clustering method is applied again. These steps are repeated until all transversal positions have been checked or the time available for performing the detection is exhausted.


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

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

Steps of the clustering method.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-10788: Steps of the clustering method.
Mentions: Figure 6 shows the main steps of the clustering method. Accordingly, once the initial solution has been created and the clustering algorithm has been executed, periodical defects placed at other transversal positions are sought. To do this, the histogram is calculated again, without taking into account the individual defects included in a periodical defect previously detected. An initial solution is created for that transversal position and the clustering method is applied again. These steps are repeated until all transversal positions have been checked or the time available for performing the detection is exhausted.

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.