Yarn strength prediction: a practical model based on Artificial Neural Networks

Abstract - Yarn strength is one of the most significant parameters to be controlled during yarn spinning process. This parameter strongly depends on both the rovings’ characteristics and the spinning process. On the basis of their expertise textile technicians are able to provide a raw and qualitative prediction of the yarn strength by knowing a series of fiber parameters like length, strength, and fineness. Nevertheless, they often need to perform many tests before producing a yarn with a desired strength. This paper describes a Feed Forward Back Propagation Artificial Neural Network-based model able to help the technicians in predicting the yarn strength without the need of physically spinning the yarn. The model performs a reliable prediction of the yarn strength on the basis of a series of roving parameters, commonly measured by the technicians before the yarn spinning process starts. The model has been trained with 98 training data and validated with 50 new tests. The mean error in prediction of yarn strength, using the validation set, is less than 4%. The results have been compared with the one obtained by means of a classical method: the multiple regression. Nowadays, the developed model is running in the laboratory of NewMill S.p.A., an important textile company that operates in Prato (Italy).