A modeling study of micro-cracking processes of polyurethane coated cotton fabrics

Abstract

Polyurethane (PU) coating became popular in recent decades to achieve water resistance in clothing fabrics with enhanced visual properties. But reduced breathability of coated fabric is a setback for the clothing industry; therefore, there have been various attempts to achieve breathable water-resistant coatings. A new and facile method of enhancing breathability of PU-coated fabrics, which has been called micro-cracking, has been recently studied and highly encouraging outcomes have been obtained for the use of the process in industry. But when any process is considered to have industrial applications, it is essential to conduct not only the optimization but also modeling studies to find out whether the outputs are predictable; the process is controllable and allows us to see how the results are affected by process parameters. This work conducts a modeling study of micro-cracking processes of PU-coated samples to complete this evaluation. For this purpose, an artificial neural network (ANN) and a least square support vector model (LS-SVM) are developed for the prediction of various properties of PU-coated fabrics after micro-cracking. The results showed that the effects of micro-cracking process on various properties of coated fabric could be predicted through ANN or LS-SVM modeling; specifically, the ANN exhibited better performance in the test set of the data. Thus, it is concluded that the results and the measurements were found to be compatible for defining the process as an industrial alternative.

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

“…(3) its implementation is easy; (4) its network topology is no need to be determined in advance, which can be generated automatically when the training process terminates; (5) it has high generalized capability which can avoid local minimum [25]. Due to these prominent advantages, SVR has been demonstrated much success in the application in textile and fashion industry, such as prediction textile dying process parameters [26], yarns characteristics [27,28], fabric qualities [29], fabric contents [30,31] and human body measurements [32]. Hence, we adopted SVR to deal with the rules of GP associate adaptation in this study.…”

Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

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