Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model |
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| Authors: | Onur Balci S Noyan O?ulata Cenk Sahin R Tu?rul O?ulata |
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| Institution: | (1) The Department of Industrial Engineering, The University of Cukurova, 01330 Adana, Turkey;(2) The Department of Textile Engineering, The University of Cukurova, 01330 Adana, Turkey |
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| Abstract: | We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex
acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural
network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models
was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data
satisfactorily such as L*, a*, b*, C, h
o and wash fastness performance using both models. The statistical findings indicated that the regression models provide more
accurate prediction for all colour data with an average error of 1% contrast to previous study. In terms of prediction of
fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon
part of adjacent multifiber. |
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| Keywords: | CIELab Wash fastness Artificial neural network Levenberg-Marquardt algorithm Linear regression model |
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