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A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

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Abstract

Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers’ perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer’s taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

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Authors and Affiliations

  1. Laboratory of Study of the Thermal and Energy Systems (LESTE), National School Engineers of Monastir, University of Monastir, Monastir, 5019, Tunisia

    Faten Fayala, Hamza Alibi & Abdelmajid Jemni

  2. GEMTEX Research Laboratory, National School of Arts and Textiles Industries (ENSAIT), University North Lille of France, Lille, 30329-59056, France

    Xianyi Zeng

Authors
  1. Faten Fayala
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  2. Hamza Alibi
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  3. Abdelmajid Jemni
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  4. Xianyi Zeng
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Correspondence to Hamza Alibi.

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Fayala, F., Alibi, H., Jemni, A. et al. A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics. Fibers Polym 16, 1417–1429 (2015). https://doi.org/10.1007/s12221-015-1417-7

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  • DOI: https://doi.org/10.1007/s12221-015-1417-7

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