Automatic damage detection of engineering ceramics ground surface based on texture analysis

Abstract

Ground textures seriously interfere with the exact identification of grinding damage. The common nondestructive testing techniques for engineering ceramics are limited by their difficulty and cost. Therefore, this paper proposes a global image reconstruction scheme in ground texture surface using Fourier transform (FT). The lines associated with high-energy frequency components in the spectrum that represent ground texture information can be detected by Hough transform (HT), and the corresponding high-energy frequency components are set to zero. Then the spectrum image is back-transformed into the spatial domain image with inverse Fourier transform (IFT). In the reconstructed image, the main ground texture information has been removed, whereas the surface defects information is preserved. Finally, Canny edge detection is used to extract damage image in the reconstructed image. The experimental results of damage detection for the ground texture surfaces of engineering ceramics have shown that the proposed method is effective.

Keywords

engineering ceramics / damage detection / ground texture / Fourier transform

References

[1] Zhang B, Zheng X L, Tokura H, et al. Grinding induced damage in ceramics[J]. Journal of Materials Processing Technology, 2003, 132(1–3): 353-364.

[2] Biagi E, Fort A, Masotti L, et al. Ultrasonic high resolution images for defect detection in ceramic materials[J]. Research in Nondestructive Evaluation, 1995, 6(4): 219-226.

[3] JOM, 1998, 50(11)

[4] Lü B, Yuan J, Deng Q, et al. Novel semifixed abrasive tool for high lapping efficiency of functional ceramics[J]. Nanotechnology and Precision Engineering, 2011, 9(6): 545-550.

[5] Tsai D M, Hsieh C Y. Automated surface inspection for directional textures[J]. Image and Vision Computing, 1999, 18(1): 49-62.

[6] Kumar A. Computer-vision-based fabric defect detection: A survey[J]. IEEE Transactions on Industrial Electronics, 2008, 55(1): 348-363.

[7] Xie X. A review of recent advances in surface defect detection using texture analysis techniques[J]. Electronic Letters on Computer Vision and Image Analysis, 2008, 7(3): 1-22.

[8] Ngan H Y T, Pang G K H, Yung N H C. Automated fabric defect detection: A review[J]. Image and Vision Computing, 2011, 29(7): 442-458.

[9] Jain A K, Ratha N K, Lakshmanan S. Object detection using Gabor filters[J]. Pattern Recognition, 1997, 30(2): 295-309.

[10] Tsai D M, Wu S K. Automated surface inspection using Gabor filters[J]. International Journal of Advanced Manufacturing Technology, 2000, 16(7): 474-482.

[11] Tsai D M, Chiang C H. Automatic band selection for wavelet reconstruction in the application of defect detection[J]. Image and Vision Computing, 2003, 21(5): 413-431.

[12] Li W C, Tsai D M. Wavelet-based defect detection in solar wafer images with inhomogeneous texture[J]. Pattern Recognition, 2012, 45(2): 742-756.

[13] Chan C H, Pang G K H. Fabric defect detection by Fourier analysis[J]. IEEE Transactions on Industry Applications, 2000, 36(5): 1267-1276.

[14] Tsai D M, Huang T Y. Automated surface inspection for statistical textures[J]. Image and Vision Computing, 2003, 21(4): 307-323.

[15] Perng D B, Chen S H, Chang Y S. A novel internal thread defect auto-inspection system[J]. The International Journal of Advanced Manufacturing Technology, 2010, 47(5–8): 731-743.

[16] Li W C, Tsai D M. Automatic saw-mark detection in multicrystalline solar wafer images[J]. Solar Energy Materials and Solar Cells, 2011, 95(8): 2206-2220.

[17] Tsai D M, Wu S C, Li W C. Defect detection of solar cells in electroluminescence images using Fourier image reconstruction[J]. Solar Energy Materials and Solar Cells, 2012, 99, 250-262.

[18] Davies E R. Image space transforms for detecting straight edges in industrial images[J]. Pattern Recognition Letters, 1986, 4(3): 185-192.