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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.


Relation between the reduction of thickness and the elongation of the strip.
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f10-sensors-12-10788: Relation between the reduction of thickness and the elongation of the strip.

Mentions: As discussed in Section 3.2.1, the theoretical period length of the rolls involved in the production process must be known before starting the detection. Thus, the time required to perform the detection can be reduced by searching only periodical defects with such periodicities, instead of iterating through a range of possible values as made by Traxler et al. [25]. In our practical application, the theoretical period length does not correspond to the perimeter of the rolls since the length of the material varies, but it can be calculated easily. The forces applied during rolling produce friction on the strip, which is lower in the transversal direction than in the forward direction of the strip, cause the stretching of the strip in a forward direction and not sideways. Figure 10 shows an example in which a defective roll generates a periodical defect whose period length is initially L (the perimeter of that roll) when the thickness of the strip is S. Passing through the next stand, the thickness of the strip is reduced to S/2. Since the volume of the steel remains constant, the length of the steel increases proportionally, causing the separation between two consecutive individual defects within the periodical defect to increase to 2L. Considering the separation between each pair of work rolls of the same stand and their radii, the theoretical period of each cylinder can be calculated by Equation (19) [31].


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

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

Relation between the reduction of thickness and the elongation of the strip.
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-12-10788: Relation between the reduction of thickness and the elongation of the strip.
Mentions: As discussed in Section 3.2.1, the theoretical period length of the rolls involved in the production process must be known before starting the detection. Thus, the time required to perform the detection can be reduced by searching only periodical defects with such periodicities, instead of iterating through a range of possible values as made by Traxler et al. [25]. In our practical application, the theoretical period length does not correspond to the perimeter of the rolls since the length of the material varies, but it can be calculated easily. The forces applied during rolling produce friction on the strip, which is lower in the transversal direction than in the forward direction of the strip, cause the stretching of the strip in a forward direction and not sideways. Figure 10 shows an example in which a defective roll generates a periodical defect whose period length is initially L (the perimeter of that roll) when the thickness of the strip is S. Passing through the next stand, the thickness of the strip is reduced to S/2. Since the volume of the steel remains constant, the length of the steel increases proportionally, causing the separation between two consecutive individual defects within the periodical defect to increase to 2L. Considering the separation between each pair of work rolls of the same stand and their radii, the theoretical period of each cylinder can be calculated by Equation (19) [31].

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.