||In recent years, there has been a short-chain revolution in the textile industry. With the delivery time of finished products getting shorter and shorter, in order to maintain a competitive advantage and reduce defects in the textile process, it is an imperative problem. However, a variety of different defects will be caused in the textile manufacturing process, and the cause of each type of defect is not the same. Therefore, how to find out the key features and propose effective optimization parameter settings to reduce the number of defects must be discussed in depth.This research uses the sequential backward selection method to find out the key features affecting the defects based on the data set of the textile process, and then establishes the regression tree model to find the leaf nodes with more low defects, and analyzes the fabric of the node. Establish rules for properties and machine parameters, and finally use statistical verification to verify whether the rules are effective in reducing the number of defects. Finally, through experiments, it is found that theoretically, it can bring 39% benefits to the enterprise at most.|
|| 紡拓會, "2019年臺灣紡織工業概況," 6 2020. [Online]. Available: https://www.textiles.org.tw/TTF/main/content/wHandMenuFile.ashx?file_id=1.|
 台灣區絲織工業同業公會, "【台灣】紡織智造掀短鏈革命," [Online]. Available: http://www.filaweaving.org.tw/news-detail/show-752381.htm.
 紡紗會訊, "紗支常見瑕疵及預防解決方法," 2017. [Online]. Available: http://www.tsa.org.tw/file/17380_20171020095637.pdf.
 K. Au, "Quality control in the knitting process and common knitting faults," in Advances in Knitting Technology, Elsevier, 2011, pp. 213-232.
 R. Gong and Y. Chen, "Predicting the performance of fabrics in garment manufacturing with artificial neural networks," Textile Research Journal, vol. 7, no. 69, pp. 477-482, 1999.
 S.-C. Hung, “Root Cause Analysis On Weaving Processes with Many-to-many Data Structure,” 2018.
 I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," Journal of Machine Learning Research 3 (2003) 1157-1182, 2003.
 R. Gutierrez-Osuna, "Pattern Analysis," [Online]. Available: http://research.cs.tamu.edu/prism/lectures/pr/pr_l11.pdf.
 B. Leo, F. Jerome, S. Charles J and O. Richard A, Classification and regression trees, 1984.
 R. Irizarry, Statistical learning: Algorithmic and nonparametric approaches, 2006.
 R. J. Lewis, "An introduction to classification and regression tree (CART) analysis," in Annual meeting of the society for academic emergency medicine in San Francisco, California, 2000.
 M. Wolfgang and W. Eckhard, "Applying decision tree methodology for rules extraction under cognitive constraints," European Journal of Operational Research, vol. 2, no. 136, pp. 282-289, 2002.
 B. B. İ. G. F. İ. İ. K. G. Ö. N. Bakır, "Defect cause modeling with decision tree and regression analysis," World Acad Sci Eng Technoly, no. 24, pp. 1-4, 2006.
 F. Gregg C, A. Kirkwood F, A. William T, Y. Clyde W, B. W John and A. S. A. C. , "Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis," Jama, vol. 5, no. 293, pp. 572-580, 2005.
 W. Hong, L. Dong, Q. Huang, W. Wu, J. Wu and Y. Wang, "Prediction of severe acute pancreatitis using classification and regression tree analysis," Digestive diseases and sciences, vol. 12, no. 56, pp. 3664-3671, 2011.
 B. Berna, B. İ, G. FA, İ. İA, K. G and Ö. NE, "Defect cause modeling with decision tree and regression analysis," World Acad Sci Eng Technoly, no. 24, pp. 1-4, 2006.
 S. Ozdemir and D. Susarla, Feature engineering made easy, 2020.
 常國珍, 趙仁乾 and 張秋劍, 一書貫通:從資料科學橫入人工智慧領域, 2020.