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Optimization of woven fabric parameters for ultraviolet radiation protection and comfort using artificial neural network and genetic algorithm

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Abstract

Optimization of woven fabric parameters for ultraviolet protection factor (UPF) and comfort properties has been attempted using hybrid artificial neural network (ANN)–genetic algorithm (GA) system. ANN was used for developing the prediction models, and GA was employed as an optimization tool. Four feasible combinations of UPF, air permeability and moisture vapor transmission rate (MVTR) were chosen from the Pareto charts of UPF–air permeability and UPF–MVTR. Penalty function method was adopted to form a single objective function by combining the objectives and constraints related to UPF, air permeability and MVTR. The developed ANN–GA hybrid system was executed to obtain the solution set of input parameters for achieving the targeted fabric properties. To validate the developed ANN–GA-based fabric parameter optimization system, four fabric samples were woven using the solution sets of input parameters and functional properties of these engineered fabrics were evaluated. The targeted and achieved values of fabric properties of four validation samples were in reasonably good agreement.

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Acknowledgments

The authors are also thankful to Centre of Excellence for Soft Computing Research, Indian Statistical Institute, Kolkata, and Department of Science and Technology, New Delhi, for providing financial support for this research work.

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Authors and Affiliations

  1. Indian Institute of Technology Delhi, Delhi, 110016, India

    Abhijit Majumdar, Apurba Das & Piyali Hatua

  2. Government College of Engineering and Textile Technology, Berhampore, 742101, India

    Anindya Ghosh

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  1. Abhijit Majumdar
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  2. Apurba Das
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  3. Piyali Hatua
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  4. Anindya Ghosh
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Correspondence to Abhijit Majumdar.

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Majumdar, A., Das, A., Hatua, P. et al. Optimization of woven fabric parameters for ultraviolet radiation protection and comfort using artificial neural network and genetic algorithm. Neural Comput & Applic 27, 2567–2576 (2016). https://doi.org/10.1007/s00521-015-2025-6

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  • DOI: https://doi.org/10.1007/s00521-015-2025-6

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