PREDICTING HAIRINESS FOR RING AND ROTOR SPUN YARNS AND ANALYZING THE IMPACT OF FIBER PROPERTIES

Models for predicting ring or rotor yam hairiness are built using a ba ck-propagation neural network algorithm. These models are based on fib er property input measured by three different systems, HVI, AFIS, and FMT. We compare the prediction results from the different models, whic h reveal that yam hairiness measurements from HVI data an superior to other models. The optimum model is based on the availability of all th ree measurement systems. We also study the impact of each fiber proper ty on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or roto r yam hairiness.