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Models for predicting ring or rotor yarn hairiness are built using a back-propagation neural network algorithm. These models are based on fiber property input measured by three different systems, hvi, afis, and fmt. We compare the prediction results from the different models, which reveal that yarn hairiness measurements from hvi data are superior to other models. The optimum model is based on the availability of all three measurement systems. We also study the impact of each fiber property on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or rotor yarn hairiness.

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