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An efficient method for defect detection during the manufacturing of web materials

Author

Listed:
  • Francisco G. Bulnes

    (University of Oviedo)

  • Ruben Usamentiaga

    (University of Oviedo)

  • Daniel F. Garcia

    (University of Oviedo)

  • J. Molleda

    (University of Oviedo)

Abstract

Defect detection is becoming an increasingly important task during the manufacturing process. The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the economic impact caused by discarding defective products. This point is especially important in the case of products that are very expensive to produce. In this paper, the authors propose a method to detect a specific type of defect that may occur during the production of web materials: periodical defects. This type of defect is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale. To run the proposed method, two different functions must be executed a large number of times. Since the time available to perform the detection of these defects may be limited, it is very important to consume the least amount of time possible. In order to reduce the overall time required for detection, an analysis of how the method accesses the input data is performed. Thus, the most efficient data structure to store the information is determined. At the end of the paper, several experiments are performed to verify that both the proposed method and the data structure used to store the information are the most suitable to solve the aforementioned problem.

Suggested Citation

  • Francisco G. Bulnes & Ruben Usamentiaga & Daniel F. Garcia & J. Molleda, 2016. "An efficient method for defect detection during the manufacturing of web materials," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 431-445, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-014-0876-9
    DOI: 10.1007/s10845-014-0876-9
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    Citations

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    Cited by:

    1. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    2. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
    3. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    4. Mohamed Ben Gharsallah & Ezzedine Ben Braiek, 2021. "Computer aided manufacturing method for surface silicon steel inspection based on an efficient anisotropic diffusion algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1025-1041, April.

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