Regressional Optimizations of Cotton Spun Yarn by Controlling its Process Parameters

Abstract Cotton is the most commonly used natural fiber and has a significant contribution to the production of yarn manufacturing. This yarn is subsequently utilized for the production of fabrics, garments, and other textile products. The quality of the end product depends on the selection of an appropriate spinning process and output parameters. Numerous methods and processes are involved in the production of yarn. Ring spinning machine is most commonly used for the production of cotton spun yarn. It is necessary to optimize the process parameters of ring-spun yarn without compromising on quality and production. In this research work; these parameters have been optimized by applying the multiple linear regression analysis. The process parameters (especially spindle speed, twist and yarn diameter) and their effect on yarn quality have been discussed in detail. Total 135 ring-spun yarn samples have been produced under three different levels of spindle speed, twist, and linear density. These yarn samples are categorized as 8 Ne, 16 Ne, and 24 Ne at three different Twist multipliers (3.8, 4.0, and 4.2) and different revolutions per minute of the spindle (9500 rpm, 10500rpm, and 11500 rpm). The models have been designed to predict the quality of ring-spun by utilizing USTER evenness tester data. The Count of yarn, yarn twist, and spindle speed were selected as a predictor. The multiple regression method has been used to find out the relation between the process parameters and yarn quality characteristics. The high values of R2 (the coefficient of determination) showed the relationships in the prediction model.