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Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models

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Abstract

In this study, an artificial neural network (ANN) and a statistical model are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Seven different blend ratios of polyester/viscose slivers are produced and these slivers are manufactured with four different rotor speed and four different yarn counts in rotor spinning machine. A back propagation multi layer perceptron (MLP) network and a mixture process crossed regression model (simplex lattice design) with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed) are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Both ANN and simplex lattice design have given satisfactory predictions, however, the predictions of statistical models gave more reliable results than ANN.

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Authors and Affiliations

  1. Textile Engineering Department, Cukurova University, 01330, Balcali-Adana, Turkey

    Oğuz Demiryürek & Erdem Koç

  2. Mechanical Engineering Department, Ondokuz Mayis University, 55139, Kurupelit-Samsun, Turkey

    Erdem Koç

Authors
  1. Oğuz Demiryürek
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  2. Erdem Koç
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Correspondence to Oğuz Demiryürek.

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Demiryürek, O., Koç, E. Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models. Fibers Polym 10, 237–245 (2009). https://doi.org/10.1007/s12221-009-0237-z

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  • DOI: https://doi.org/10.1007/s12221-009-0237-z

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