A new method for a fabric defect identifying system uses fuzzy inference in multicondition approximate reasoning and is capable of defect identification. The system uses fuzzy inference rules, and the membership function for these rules adopts a neural network approach. Only a small number of fuzzy inference rules are required to make the identifications of nondefect, slub (warp direction), slub (weft direction), nep, and composite defect. One fuzzy inference rule can replace many crisp rules. With this method, we can design a reliable system for identifying fabric defects. Experimental results with this approach have demonstrated an identification ability comparable to that of a human inspector.
Get full access to this article
View all access options for this article.
References
1.
1. ASTM Standards, D3990-93, Standard terminology relating to fabric defects.
2.
2. Barrett, G. R., Clapp, T. G., and Titus, K. J., An On-Line Fabric Classification Technique Using a Wavelet-Based Neural Network Approach, Textile Res. J.66, 521–528 (1996).
3.
3. Chen, P. W., Liang, T. C., Yau, H. F., Sun, W. L., Wang, N. C., Lin, H. C., and Len, R. C., Classifying Textile Faults with a Back-Propagation Neural Network Using Power Spectra, Textile Res. J.68, 121–126 (1998).
4.
4. Cox, E., Fuzzy Fundamentals, IEEE Spectrum, 58-61 (1992).
5.
5. Fan, K. C., Wang, Y. K., Chang, B. L., Wang, T. P., Jou, C. H., and Kao, I. F., Fabric Classification Based on Recognition Using a Neural Network Dimensionality Reduction, Textile Res. J.68, 179–185 (1998).
6.
6. Freeman, J., The Modeling of Spatial Relations, Computer Graph. Image Process.4, 156–171 (1975).
7.
7. Gong, R. H., and Chen, Y., Predicting the Performance of Fabrics in Garment Manufacturing with Artificial Neural Networks, Textile Res. J.69, 477–482 (1999).
8.
8. Huang, C. C., and Yu, W. H., Control of Dye Concentration, pH, and Treatment in Dyeing Processes, Textile Res. J.69, 914–918 (1999).
9.
9. Jurada, J. M., “Introduction to Artificial Neural Networks,” West Publishing Company, MN, 1992.
10.
10. Klir, G. J., and Yuan, B., “Fuzzy Sets and Fuzzy Logic,” Prentice-Hall, NJ, 1995.
11.
11. Klir, G. J., Clair, U. S., and Yuan, B., “Fuzzy Set Theory,” Prentice-Hall, NJ, 1997, pp. 222–230.
12.
12. Park, C. K., and Kang, T. J., Objective Evaluation of Seam Pucker Using Artificial Intelligence, Part I: Geometric Modeling of Seam Pucker, Textile Res. J.69, 735–742 (1999).
13.
13. Park, C. K., and Kang, T. J., Objective Evaluation of Seam Pucker Using Artificial Intelligence, Part II: Method of Evaluating Seam Pucker, Textile Res. J.69, 835–845 (1999).
14.
14. Park, C. K., and Kang, T. J., Objective Rating of Seam Pucker Using Neural Networks, Textile Res. J.67, 494 (1997).
15.
15. Tou, J. T., and Gonzalez, R. C., “Pattern Recognition Principles,” Addison Wesley, NY, 1974.
16.
16. Tsai, I. S., Lin, C. H., and Lin, J. J., Applying an Artificial Neural Network to Pattern Recognition in Fabric Defects, Textile Res. J.65, 123–130 (1995).
17.
17. Vroman, P., Happiette, M., and Rabenasolo, B., Fuzzy Adaptation of Holt-Winter Model for Textile Sales-forecasting, J. Textile Inst.89, 78–89 (1998).
18.
18. Zadeh, L. A., The Concept of a Linguistic Variable and Its Application to Approximate Reasoning, Info. Sci.8, Part 1, 199–249, Part 2, 301-357 (1975).
