Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Abstract

Abstract Most textile products incorporate yarn as a fundamental element in the production process. Among various yarn manufacturing methods, the ring spinning system stands out as a crucially employed technology due to its advantages including yarn quality, evenness, low hairiness, and ease of handling. The parameters of drafting zone in this technology have a great impact on the quality of yarns. Typically, tuning this drafting zone parameters is time-consuming and costly through trial and error method. This study introduces an algorithmic procedure based on response surface methodology (RSM), experimental modeling, and multi-objective optimization to reduce unevenness percentage (U%) and imperfection index (IPI). Input parameters, including cots hardness of front and back top rollers, spacer size, and break draft, are optimized. Results indicate superior prediction performance of the artificial neural network (ANN) (average vaule of TGF = 1.9996) compared to RSM (average value of TGF = 1.8668). Consequently, ANN is selected for optimization. Furthermore, coupling the genetic algorithm with two ANN-based models reduced IPI from 39 to 33.67 and a reduction from 9.73–9.67% occurred in terms of U%. The final setting of Input parameters were cots hardness of front roller of 70 shore and cots hardness of back roller of 76 shore, spacer size 2.8 mm, and break draft of 1.26. This method efficiently optimizes the drafting zone parameter, enhancing yarn quality.