Building a Rule Set for the Fiber-to-Yarn Production Process by Means of Soft Computing Techniques

摘要

An important aspect of the spinning process is the ability to predict the spinnability of a yarn and its resulting strength based on the fiber quality and machine settings. Currently available fiber-to-yarn models are limited to the so-called "black box" approach, generating an output (spinnability) without containing physical, interpretable information about the process itself. This paper presents a method to predict the spinnability and strength of a yarn with a set of IF-THEN rules. The rule set is automatically genrated using the available data by means of a new learning classifier system called a fuzzy efficiency-based classifier system (FECS), which enhances the original learning classifier algorithm of Goldberg [5] by defining several rule efficiencies and introducing them into the learning strategy of the system.