Mining Rule of Quality Control for Spinning Process with Rough Set Theory
Qian Xiang, Zhi Jun Lv, Jian Guo Yang, Xiang Gang Yin · Applied Mechanics and Materials · 2011
Due to absence of an integral mathematical model, quality control in spinning process has been hard problem for a long time. Rough sets theory (RST) is a methodology that effectively deals with the problems with inexact, uncertain or vague knowledge in a complex information system. Considering a mass of data from spinning process and inspection, as well as the variety of knowledge and experience from domain experts, an RST-based intelligent control model for spinning process is presented in this paper. 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.