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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.

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In this research it is aimed to predict fabrics' air permeability properties by ANNs (artificial neural networks) before production with using inputs like some fabric parameters and finishing treatments. For this aim 27 various fabrics were weaved. After dyeing finishing treatments for antipilling were applied to fabrics in 3 concentrations. ANN models were established to predict fabrics' air permeability values with the selected 6 inputs such as weft yarn number, weft density, weaving pattern, fabric weight, fabric thickness and finishing treatment concentrations. The best results whose regression degree is R = 0.99366, were obtained with two hidden layer networks with 5 neurons.

In this study fabric stiffness/softness is examined which is an important element of applications on finishing processes of fabric. It is also studied the prediction of the fabric stiffness/softness with help of different parameters. Specific to this aim three different weft densitoes (30 tel/cm), 3 different yarn numbers (20/1, 24/1, 30/1 Nm) and 3 different weaving patterns were used and 27 different fabrics were weaved. During the weaving process warp yarn is 100% polyester and weft yarn is 67-33% cotton/polyester. Three different finishing processes are applied to the 27 different fabrics (softness finishing treatment, crosslinking finishing and antipilling finishing) in 3 different concentrations and at the end there are 243 sample fabrics gathered. Stiffness test was applied to the samples according to the ASTM (American Society for Testing and Materials) D 4032-94 the Circular Bending Method. Test results were evaluated statistically. It was seen that the established model was related with p < 0.0001 also, Artificial Neural Network (ANN) model was formed in order to predict the fabric softness using the test results. MATLAB packet model was used in forming the model. ANN was formed with 5 inputs (fabric plait, weft yarn no, weft density, weft type, finishing concentration) and 1 output (stiffness). ANN model was established using feed forward-back propagation network. There were many trials in forming the ANN and the best results were gathered at the values established with 0.97317 regression value, 2 hidden layers and 10 neurons.