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Abstract

The main advantage of artificial neural network (ANN) approach over theoretical methods lies in the fact that it does not require the accurate mathematical model of the system while modeling complicated nonlinear processes. This paper presents an ANN model for predicting initial load-extension behavior of plain weave and plain weave derivative fabrics. A single hidden layer feed-forward ANN based on a back-propagation algorithm with four input neurons and one output neuron was developed to predict initial modulus in the warp and weft directions. Input values are defined as combination expressions of geometrical parameters of fabric and yarn flexural rigidity, which were obtained from Leaf’s mathematical model. Data were divided into two groups as training and test sets. A very good agreement between the examined and predicted values was achieved.

Keywords

artificial neural network initial load-extension modeling plain-woven fabric

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The Prediction of Initial Load-extension Behavior of Woven Fabrics Using Artificial Neural Network

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