Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

Hossain, I. and Choudhury, I.A. and Mamat, A. and Shahid, A. and Khan, A.N. and Hossain, A. (2016) Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique. Advances in Fuzzy Systems, 2016. pp. 1-9. ISSN 1687-7101

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Official URL: http://dx.doi.org/10.1155/2016/3632895

Abstract

The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.

Item Type: Article
Uncontrolled Keywords: Mechanical Properties; Viscose/Lycra Knitted Fabrics; Fuzzy Technique
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Jul 2017 06:22
Last Modified: 14 Jul 2017 06:22
URI: http://eprints.um.edu.my/id/eprint/17513

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