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Article
Affiliation(s)

1. Sinop University, Gerze Vocational School, Sinop 57600, Turkey
2. Çukurova University, Textile Engineering Department, Adana 01330, Turkey

ABSTRACT

In this research it is aimed to predict fabric stiffness by ANNs (artificial neural networks) using inputs like some fabric parameters and finishing treatments. For this aim 27 various fabrics were weaved with using 3 different weft densities, 3 different weft yarn sizes, 3 different weaving patterns. The fabrics were produced of using 100% Pes on the warp yarn and 100% cotton on the weft yarn. And 3 concentrations of 2 finishing treatments were applied on the 27 various fabrics. The stiffness properties of fabrics were tested by stiffness tester with using ASTM (American society for testing and materials) D 4032-94 circular bending test method. Then prediction models were tried to be established with using production parameters for inputs and measured stiffness values for outputs by ANN techniques at MATLAB® programme. ANN models were established to predict fabric stiffness with the selected 5 inputs such as weft yarn number, weft density, weaving pattern, finishing treatments and concentrations. While the network models were established, it was used feed-forward and back propagation network. While the network models were established with the aim of determining optimum network, 10 alternative models were established by changing of transfer function, neuron numbers and number of hidden layers. The best results whose regression degree is R = 0.96, were obtained with two hidden layer networks with 30 neurons.

KEYWORDS

Stiffness, softness, prediction, ANN.

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