In this study artificial neural network (ANN) models have been designed to predict the ring cotton yarn properties from the fiber properties measured on HVI (high volume instrument) system and the performance of ANN models have been compared with our previous statistical models based on regression analysis. Yarn count, twist and roving properties were selected as input variables as they give significant influence on yarn properties. In experimental part, a total of 180 cotton ring spun yarns were produced using 15 different blends. The four yarn counts and three twist multipliers were chosen within the range of Ne 20-35 and e 3.8-4.6 respectively. After measuring yarn tenacity and breaking elongation, evaluations of data were performed by using ANN. Afterwards, sensitivity analysis results and coefficient of multiple determination (R 2 ) values of ANN and regression models were compared. Our results show that ANN is more powerful tool than the regression models.

The objective of this second part of the study is to develop artificial neural network models for the prediction of yarn hairiness and unevenness and to compare the performance of ANN models with our previous statistical models based on regression analysis. Besides HVI properties, yarn count, twist and roving properties were also selected as input variables. Part 1 provided detailed description of experimental procedure of the study. Yarn hairiness and unevenness tests were performed on Uster Tester 3. Following the developed ANN models, sensitivity analysis results and coefficient of multiple determination (R 2 ) values of ANN and regression models were compared. Analyses are showed that ANN models improve the prediction performance with regards to regression models.