This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters.
In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process.
The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN=0.989> RMLR=0.901).
The NPF measurement method is limited at low speed.
Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.
The authors greatly appreciate the guidance and help given by Prof. Jafargholi Amirbayat, and also Khoy Textile Company for the materials supplied.
Haghighat, E., Mohammad Etrati, S. and Shaikhzadeh Najar, S. (2013), "Modeling of needle penetration force in denim fabric", International Journal of Clothing Science and Technology, Vol. 25 No. 5, pp. 361-379. https://doi.org/10.1108/IJCST-01-2012-0031Download as .RIS
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