Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems
- 96 Downloads
Subjective and objective evaluations of the handle of textile materials are very important to describe its tactile comfort for next-to-skin goods. In this paper, the applicability of artificial neural-network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modeling approaches for the prediction of the psychological perceptions of functional fabrics from mechanical properties were investigated. Six distinct functional fabrics were evaluated using human subjects for their tactile score and total hand values (THV) using tactile and comfort-based fabric touch attributes. Then, the measurement of mechanical properties of the same set of samples using KES-FB was performed. The RMSE values for ANN and ANFIS predictions were 0.014 and 0.0122 and are extremely lower than the variations of the perception scores of 0.644 and 0.85 for ANN and ANFIS, respectively with fewer prediction errors. The observed results indicated that the predicted tactile score and THV are almost very close to the actual output obtained using the human judgment. Fabric objective measurement technology, therefore, provides reliable measurement approaches for functional fabric quality inspection, control, and design specification.
KeywordsANFIS ANN Mechanical properties Total hand value Tactile comfort
Funding note: Open access funding provided by University of Boras.
- 1.R. Nayak, L. Wang, and R. Padhye in “Electronic Textiles: Smart Fabrics and Wearable Technology”, 1st ed. (T. Dias Ed.), pp.239-256, Elsevier, Amsterdam, 2015.Google Scholar
- 3.T. Dias and A. Ratnayake in “Electronics Textiles:Smart Fabrics and Wearable Technology”, 1st ed. (T. Dias Ed.), pp.110–145, Elsevier, Amsterdam, 2015.Google Scholar
- 8.V. T. Bartels in “Handbook of Medical Textiles” (V. T. Bartels Ed.), pp.221–247, Woodhead Publishing, Oxford, 2011.Google Scholar
- 10.L. M. Sztandera, Proc. 8th WSEAS Int. Conf. Appl. Comput. Sci., 221 (2008).Google Scholar
- 17.S. E.-G. Jeguirim, D. C. Adolphe, M. Sahnoun, A. B. Douib, L. M. Schacher, and M. Cheikhrouhou, J. Eng. Fiber. Fabr., 7, 88 (2012).Google Scholar
- 18.L. M. Sztandera Proc. 8th WSEAS Int. Conf. Appl. Comput. Sci., 217 (2008).Google Scholar
- 19.T. Melkie Getnet, R. Harpa, Y. Chen, L. Wang, V. Nierstrasz, and C. Loghin, J. Ind. Text., doi:10.1177/ 1528083718764906 (2018).Google Scholar
- 22.F. Sun, C. Sun, C. Chen, Z. Du, and W. Yu, Text. Res. J., doi:10.1177/0040517517690624 (2018).Google Scholar
- 23.R. J. Schalkoff, “Artificial Neural Networks”, Vol. 1, pp.422–451, McGraw-Hill, New York, 1997.Google Scholar
- 25.W. Suparta and K. M. Alhasa in “Modeling of Tropospheric Delays Using ANFIS” (W. Suparta Ed.), pp.5–18, Springer, Cham, 2016.Google Scholar
- 26.N. Gupta, Network Complex. Syst., 3, 24 (2013).Google Scholar
- 27.W. Duch and N. Jankowski, Neural Comput. Survey, 2, 163 (1999).Google Scholar
- 28.T. Terano, K. Asai, and M. Sugeno, “Fuzzy Systems Theory and Its Applications”, Academic Press Professional Inc., San Diago, 1992.Google Scholar
- 30.T. Takagi and M. Sugeno in “Readings in Fuzzy Sets for Intelligent Systems” (D. Dubois Ed.), pp.387–403, Elsevier, New York, 1993.Google Scholar
- 31.W. Suparta and K. M. Alhasa, in: Space Sci. Comm. (IconSpace), IEEE Int. Conf. on, 2013.Google Scholar
- 32.R. A. Raj, M. D. Anand, K. L. D.D. Wins, and A. S. Varadarajan, Indian J. Sci. Technol., 9, 1 (2016).Google Scholar
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.