Study of the Hairiness of Polyester-Viscose Blended Yarns. Part III - Predicting Yarn Hairiness Using an Artificial Neural Network

The hairiness of blended yarns is influenced by several parameters at the ring frame. For this reason, it is necessary to develop a model based on experimental evidence that includes all known processing factors. The generalised from of this model is a candidate for predicting yarn hairiness. In this paper, an artificial neural network and multiple linear regression were used for modelling and predicting the hairiness of polyester-viscose blended yarns based on various process parameters. The models developed were assessed by applying PF/3, the Mean Square Error (MSE), and the Correlation Coefficient (R-value) between the actual and predicted yarn hairiness. The results indicated that the artificial neural network has better performance (R = 0.967) in comparison with multiple linear regression (R = 0.878).

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