Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

Abstract EN

One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future.

Cotton wastes collected from ginning process have desirable properties which could be used during spinning process.

The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CVm%) of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm.

Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters.

The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons) were optimized to get the best predictive models.

The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network.

The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.