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Computer aided manufacturing method for surface silicon steel inspection based on an efficient anisotropic diffusion algorithm

Author

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  • Mohamed Ben Gharsallah

    (Higher National Engineering School of Tunis ENSIT)

  • Ezzedine Ben Braiek

    (Higher National Engineering School of Tunis ENSIT)

Abstract

Quality control in silicon steel manufacturing process is a crucial step. The application of image processing techniques is very useful in steel inspection and manufacturing. It has established to be the most reliable and promising solution for the development of an automatic defect detection. Since the surface of the silicon steel strip has a cluttered background and defects with small sizes, flaws detection becomes a complex task. In this paper a novel rapid algorithm based on anisotropic diffusion and saliency map is proposed for detection of defects in images of hot rolled silicon steel. The algorithm first adopted a saliency map to enhance defects. Then the computed saliency map was employed in the anisotropic diffusion coefficient function as an orientation guide of the diffusion flow. The aim behind using salient feature is that a small defect can frequently attract attention of human eyes which permits to identify defects in high textured image. Finally, the defects were extracted using a local threshold operator. To verify the validity of the proposed algorithm, extensive experiments were realized on an image database of silicon steel strip then a comparison with traditional diffusion algorithms was given. Experimental results show that this method achieves accuracy and outperforms traditional methods in terms of accuracy and robustness.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01601-1
    DOI: 10.1007/s10845-020-01601-1
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    References listed on IDEAS

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    1. Kechen Song & Yunhui Yan, 2013. "Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, December.
    2. Ssu-Han Chen & Der-Baau Perng, 2016. "Automatic optical inspection system for IC molding surface," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 915-926, October.
    3. 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.
    4. Te-Hsiu Sun & Fang-Cheng Tien & Fang-Chih Tien & Ren-Jieh Kuo, 2016. "Automated thermal fuse inspection using machine vision and artificial neural networks," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 639-651, June.
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    Cited by:

    1. Shuai Ma & Kechen Song & Menghui Niu & Hongkun Tian & Yunhui Yan, 2024. "Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 367-386, January.

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