Tuning drafting zone parameters for polyester yarn within a ring spinning system: modeling and optimization

Yarn is a fundamental element in most textile products. Among various yarn manufacturing methods, the ring spinning system is particularly important due to its benefits, such as high yarn quality, evenness, low hairiness, and ease of handling. The parameters of the drafting zone in this system greatly impact yarn quality. Typically, adjusting these parameters in the drafting zone is time-consuming and costly using trial-and-error method. This study introduces an algorithmic approach using response surface methodology (RSM), experimental modeling, and multi-objective optimization to decrease unevenness percentage (U%) and imperfection index (IPI). Input parameters optimized include cots hardness of front and back top rollers, spacer size, and break draft. Results showed that the artificial neural network (ANN) predicts response parameters superiorly with determination coefficient close to 1, compared to RSM, which has a determination coefficient of about 0.72. Therefore, ANN was chosen for optimization. Additionally, combining the genetic algorithm (GA) with two ANN-based models reduced IPI from 39 to 33.67 and a decreased from 9.73% to 9.67% occurred in terms of U%. The final Input settings were the cots hardness of the front roller of 70 shores and the cots hardness of the back roller of 76 shores, spacer size 2.8 mm, and break draft of 1.26. This method efficiently optimizes the drafting zone parameter, thus enhancing yarn quality.