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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.


Forward search with lost defects.
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f7-sensors-12-10788: Forward search with lost defects.

Mentions: If there are no defects within the area defined by Equation (15) (or none satisfies the conditions Equations (12,13,14), the feature pd.u is increased by one and the feature pd.h is updated by Equation (9). In addition, the search area must be defined taking into account this situation. To do this, an auxiliary variable α is defined, with a default value of 1. Each time an individual defect is inserted into the set, the value of α is reset to 1. Like pd.u, when there are no individual defects within the search area its value is incremented by one. Thus, the search can continue even when individual defects were not inserted into the set, as shown in Figure 7. The general expression for defining the search area during the forward search is shown in Equation (16).


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

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

Forward search with lost defects.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-10788: Forward search with lost defects.
Mentions: If there are no defects within the area defined by Equation (15) (or none satisfies the conditions Equations (12,13,14), the feature pd.u is increased by one and the feature pd.h is updated by Equation (9). In addition, the search area must be defined taking into account this situation. To do this, an auxiliary variable α is defined, with a default value of 1. Each time an individual defect is inserted into the set, the value of α is reset to 1. Like pd.u, when there are no individual defects within the search area its value is incremented by one. Thus, the search can continue even when individual defects were not inserted into the set, as shown in Figure 7. The general expression for defining the search area during the forward search is shown in Equation (16).

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.