A Novel Data Mining Method on Quality Control within Spinning Process
Rough sets theory (RST) is a new data mining method that effectively deals with the problems with inexact, uncertain or vague knowledge in a complex information system. This paper investigates knowledge discovery methods from the textile industrial database, and then presents a RST-based intelligent control model (ICM) for spinning process. In order to analyze the yarn strength when the characteristics of fibers are given, a rule extraction method based on RST is researched. The logical rules extracted from the decision table indicate that the initial strength of fibers is a key factor influencing on the yarn strength. At the same time, the different values combination of the final reduced attributes also obviously influence on the yarn strength in different degree when the certain nominal yarn is being processed. Therefore, RST method can be taken into account for spinners to choose suitable fiber materials in order to ensure the quality and reduce cost.
Data mining Rough sets Rule extraction Quality control Spinning process
Zhi-jun Lv Qian Xiang Jian-guo Yang
Engineering Research Center of Advanced Textile Machinery, Ministry of Education,Shanghai 201620, Ch College Of Mechanical Engineering, Donghua University, Shanghai 201620, China
国际会议
黄山
英文
87-92
2012-07-10（万方平台首次上网日期，不代表论文的发表时间）