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A monarch butterfly optimization-based neural network simulator for prediction of siro-spun yarn tenacity

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Abstract

Yarn tenacity directly affects the winding and knitting efficiency as well as warp and weft breakages during weaving process and therefore, is considered as the most important parameter to be controlled during yarn spinning process. Yarn tenacity is dependent on fiber properties and process parameters. Exploring the relationship between fiber properties, process parameters and yarn tenacity is very important to optimize the selection of raw materials and improve yarn quality. In this study, an efficient monarch butterfly optimization-based neural network simulator called MBONN was developed to predict the tenacity of siro-spun yarns from some process parameters and fiber properties. To this end, an experimental dataset was obtained with fiber fineness, yarn twist factor, yarn linear density and strand spacing as the input variables and yarn tenacity as the output parameter. In the proposed MBONN, a monarch butterfly optimization algorithm is applied as a global search method to evolve weights of a multilayer perception (MLP) neural network. The prediction accuracy of the MBONN was compared with that of a MLP neural network trained with back propagation algorithm, MLP neural network trained with genetic algorithms and linear regression model. The results indicated that the prediction accuracy of the proposed MBONN is statistically superior to that of other models. The effect of fiber fineness, yarn linear density, twist factor and strand spacing on yarn tenacity was investigated using the proposed MBONN. Additionally, the observed trends in variation of yarn tenacity with fiber and process parameters were discussed with reference to the yarn internal structure. It was established that higher migration parameters result in increasing the siro-spun yarn tenacity. It was found that the yarns with higher migration parameters benefit from a more coherent self-locking structure which severely restricts fiber slippage, thereby increasing the yarn tenacity.

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Notes

  1. \( ceil\left( x \right) \) rounds \( x \) to the nearest integer greater than or equal to \( x \).

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This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Textile Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Parham Soltani

  2. Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran

    Esmaeil Hadavandi

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  1. Parham Soltani
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  2. Esmaeil Hadavandi
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Correspondence to Parham Soltani.

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Soltani, P., Hadavandi, E. A monarch butterfly optimization-based neural network simulator for prediction of siro-spun yarn tenacity. Soft Comput 23, 10521–10535 (2019). https://doi.org/10.1007/s00500-018-3624-9

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