Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network

Abstract

In this study, an artificial neural network (ANN) model is presented in order to predict the tenacity and hairiness of carded cotton yarns. Fiber measurement values generated by using a high-volume instrument (HVI) and an advanced fiber information system (AFIS) were used in the ANN model as input parameters. The radial basis function neural network (RBFNN) was used as ANN structure. The best RBFNN model was determined by analyzing the effect of epochs and the number of neurons on prediction performance. By using this ANN structure, the comparison between the performance of predicting yarn properties from HVIs and from AFISs was carried out. In the study, four different yarn counts (Ne20, Ne24, Ne30, and Ne40) for 10 different blends were applied. Each yarn count was spun at 4.34αe twist factor. In this study, the model presented a good rate of accuracy for predicting yarn tenacity and hairiness by using HVI and AFIS fiber values. The study showed that there was no significant difference between the accuracy of predicting these yarn properties from HVI fiber measurement results and those from an AFIS by using the RBF. From the results, it was noted that the performance of predicting yarn hairiness was better than that of predicting yarn tenacity. Also, this study could provide researchers with exclusive information on how to select the most appropriate ANN architecture and how to evolve the model for testing.