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Inventi Impact: Modeling & Simulation

Articles

  • Inventi:ems/4/11
    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
    30-Sep-2011 Research 2011 : October - December
    Mohsen Shanbeh, Hossein Hasani, Somayeh Akhavan Tabatabaei

    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 (C VM%) 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

    How to Cite this Article
    CC Compliant Citation: Mohsen Shanbeh, Hossein Hasani, and\r\nSomayeh Akhavan Tabatabaei, Modelling and Predicting the\r\nBreaking Strength and Mass Irregularity of Cotton Rotor-Spun\r\nYarns Containing Cotton Fiber Recovered from Ginning Process\r\nby Using Artificial Neural Network Algorithm, Hindawi\r\nPublishing Corporation Modelling and Simulation in Engineering\r\nVolume 2011, Article ID 591905.
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