Selection of raw material parameters for multi-response optimization of cotton yarn qualities

In this work, a multi-response optimization of cotton yarn quality using desirability function approach has been attempted. Being a natural product, cotton yarn qualities are primarily governed by raw material characteristics. This work aims to resolve the complexity of simultaneous optimization of raw material properties using a hybrid multi-response optimization model, where predictive power of support vector regression and optimization capability of genetic algorithm are employed with desirability function. The individual desirability of cotton fibre qualities is assessed from the six properties, such as fibre strength, elongation, fineness, upper half mean length, uniformity index and short fibre content. The yarn quality parameters, such as yarn strength, yarn elongation, hairiness and unevenness, are combined together to express overall desirability. The optimum cotton quality parameters essential to produce good quality yarn can be determined from the proposed multi-response optimization model.