Modeling the Moisture and Heat Transfer of Warp Knitted Spacer Fabrics Using Artificial Neural Network Algorithm

Spacer fabrics are triple-layer structures consisting of two separate outer and one interfacing monofilament middle layers. The middle interfacing layer connects the two outer layers together. Incorporation of the middle layer imparts unique transfer properties to the fabric. In this study, models capable of predicting heat and moisture transfer of warp knitted spacer fabrics based on artificial neutral network technique were developed. The best predictive model was found by manipulation of network topology, epoch and activation functions. These models predicted transfer properties of the spacer fabrics using parameters such as fabric mass, fiber composition, porosity and thickness. Models were trained using thirty commercially available samples of warp knitted spacer fabrics. The prediction power of obtained models was evaluated with five commercially available samples out of training data. The error percentage of prediction was less than 10% for both obtained models which confirmed the validity of the developed artificial neural network models as a reliable tool in prediction the heat and moisture transfer of warp knitted spacer fabrics.

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