Abstract
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.
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09 September 2019
The article Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems, written by Melkie Getnet Tadesse, Yan Chen, Lichuan Wang, Vincent Nierstrasz, and Carmen Loghin, was erroneously originally published electronically on the publisher’s internet portal (currently SpringerLink) on 15 February 2019 without open access. After publication this was corrected and the copyright of the article changed in April 2019 to © The Author(s) 2019 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (<ExternalRef><RefSource>http://http://creativecommons.org/licenses/by/4.0/</RefSource><RefTarget Address="http://creativecommons.org/licenses/by/4.0/" TargetType="URL"/></ExternalRef>), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as 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.
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References
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.
J. Berzowska, Textile, 3, 58 (2005).
T. Dias and A. Ratnayake in “Electronics Textiles:Smart Fabrics and Wearable Technology”, 1st ed. (T. Dias Ed.), pp.110–145, Elsevier, Amsterdam, 2015.
V. Bartels, “Hnadbook of Medical Textiles”, pp.18–50, Woodhead, Oxford, 2011.
N. V. Bhat, D. T. Seshadri, M. M. Nate, and A. V. Gore, J. Appl. Polym. Sci., 102, 4690 (2006).
M. G. Tadesse, C. Loghin, Y. Chen, L. Wang, D. Catalin, and V. Nierstrasz, Smart Mater. Struct., 26, 065016 (2017).
M. G. Tadesse, D. Dumitrescu, C. Loghin, Y. Chen, L. Wang, and V. Nierstrasz, J. Electron. Mater., 47, 2082 (2018).
V. T. Bartels in “Handbook of Medical Textiles” (V. T. Bartels Ed.), pp.221–247, Woodhead Publishing, Oxford, 2011.
H. Behery, “Effect of Mechanical Propeeties on Fabric Hand”, pp.81–105, Woodhead, Boca Raton, 2005.
L. M. Sztandera, Proc. 8th WSEAS Int. Conf. Appl. Comput. Sci., 221 (2008).
S. Kawabata and M. Niwa, Int. J. Cloth. Sci. Tech., 3, 7 (1991).
F. T. Peirce, J. Text. Inst. Trans., 21, T377 (1930).
S.-W. Park, Y.-G. Hwang, B.-C. Kang, and S.-W. Yeo, Text. Res. J., 70, 675 (2000).
X. Zeng and L. Koehl, Int. J. Intell. Syst., 18, 355 (2003).
S. E.-G. Jeguirim, A. B. Dhouib, M. Sahnoun, M. Cheikhrouhou, L. Schacher, and D. Adolphe, J. Intell. Manuf., 22, 873 (2011).
X. Luo, W. Hou, Y. Li, and Z. Wang, Comput. Math. Appl., 53, 1840 (2007).
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).
L. M. Sztandera Proc. 8th WSEAS Int. Conf. Appl. Comput. Sci., 217 (2008).
T. Melkie Getnet, R. Harpa, Y. Chen, L. Wang, V. Nierstrasz, and C. Loghin, J. Ind. Text., doi:10.1177/ 1528083718764906 (2018).
X. Zeng, D. Ruan, and L. Koehl, Math. Comput. Simul., 77, 443 (2008).
S. E. G.G. Jeguirim, A. B. Dhouib, M. Sahnoun, M. Cheikhrouhou, N. Njeugna, L. Schacher, and D. Adolphe, J. Sens. Stud., 25, 201 (2010).
F. Sun, C. Sun, C. Chen, Z. Du, and W. Yu, Text. Res. J., doi:10.1177/0040517517690624 (2018).
R. J. Schalkoff, “Artificial Neural Networks”, Vol. 1, pp.422–451, McGraw-Hill, New York, 1997.
K. L. Hsu, H. V. Gupta, and S. Sorooshian, Water Resour. Res., 31, 2517 (1995).
W. Suparta and K. M. Alhasa in “Modeling of Tropospheric Delays Using ANFIS” (W. Suparta Ed.), pp.5–18, Springer, Cham, 2016.
N. Gupta, Network Complex. Syst., 3, 24 (2013).
W. Duch and N. Jankowski, Neural Comput. Survey, 2, 163 (1999).
T. Terano, K. Asai, and M. Sugeno, “Fuzzy Systems Theory and Its Applications”, Academic Press Professional Inc., San Diago, 1992.
J. S. Jang, IEEE Trans. Syst. Man. Cyb., 23, 665 (1993).
T. Takagi and M. Sugeno in “Readings in Fuzzy Sets for Intelligent Systems” (D. Dubois Ed.), pp.387–403, Elsevier, New York, 1993.
W. Suparta and K. M. Alhasa, in: Space Sci. Comm. (IconSpace), IEEE Int. Conf. on, 2013.
R. A. Raj, M. D. Anand, K. L. D.D. Wins, and A. S. Varadarajan, Indian J. Sci. Technol., 9, 1 (2016).
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A correction to this article is available at https://doi.org/10.1007/s12221-019-2000-1
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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.
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Tadesse, M.G., Chen, Y., Wang, L. et al. Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems. Fibers Polym 20, 199–209 (2019). https://doi.org/10.1007/s12221-019-8301-9
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DOI: https://doi.org/10.1007/s12221-019-8301-9