The present investigation provides a promising tool for engineering industrial products design. In fact, two soft computing approaches, namely artificial neural network (ANN) and fuzzy inference system (FIS), have been applied to model the relationship between sensory properties and instrumental measurements of knitted fabrics. The prediction performance of these models was evaluated using the root mean square error (RMSE). The obtained results show the models’ ability to predict tactile sensory attributes from the measured surface and compression properties. These neural and fuzzy models may help textile industrialists to satisfy the specific needs of consumers.
ISO.1985. Sensory analysis: Methodology. General guidance. In ISO-standard 6658, The International Organization for Standardization, Geneva, Switzerland.
2.
ISO.1992. Sensory analysis: Vocabulary. In ISO-standard 5492, The International Organization for Standardization, Geneva, Switzerland.
3.
MeilgaardM., and CivilleG.V., CarrB.T.1999. Sensory Evaluation Techniques, CRC Press LLC, ISBN 0–8493–0276–5, USA.
4.
American Association of Textile Chemists and Colorists (AATCC).2006. Evaluation Procedure 5: Guidelines for the subjective evaluation of fabric hand. In AATCC Technical Manual (pp. 684–685). New York: Howes
5.
KawabataS.1975. The standardization and analysis of hand evaluation (1st ed.). Osaka: The Textile Machinery Society of Japan.
6.
KawabataS.1980. The standardization and analysis of hand Evaluation.The textile machinery Society of Japan, Osaka, Japan.
7.
KawabataS., PostleR., and NiwaM.1982. Objective specification of fabric quality, mechanical properties and performance. Japan–Australian Joint Symposium.Kyoto: The Textile Machinery Society of Japan.
8.
TesterD., and De BoosA.1990. Get it right FAST time. Textile Horizons, 10(8), 13.
9.
PanN., and RYnK.C.1992. Physical explanations of fabric extracting curve for fabric handle evaluation. Textile Research Journal, 62, 279–290.
10.
PanN., ZeronianH., and RyuH.S.1993. An alternative approach to the objective measurement of fabrics. Textile Research Journal, 63, 33–43.
11.
ValentinD. (2001). Cours de statistiques multivariées, DESS Gestion des propriétés sensorielles des aliments, ENSBANA.
12.
ZengX., RuanD., KoehlL.2008. Intelligent sensory evaluation: Concepts, implementations and applications, Mathematics and Computers in Simulation, 77, 443–452.
13.
El-Ghezal JEGUIRIMS., Babay DhouibA., SahnounM., CheikhrouhouM., SchacherL., and AdolpheD.2010. a. Sensory and instrumental techniques evaluating the effect of structure parameters on the tactile properties of knitted fabrics, Journal of Texture Studies, 41(5), 714–735.
14.
BishopD.P.1996. Fabrics: Sensory and Mechanical Properties, Textile Progress, The Textile Institute26.
15.
DanzartM.1998. Evaluations Sensorielles: Manuel Méthodologique, SSHA, 2nd Ed, 218–317.
16.
MatsuoT., NasuN., SaitoM. (1971). Study on the Hand, part 2: The Method for Measuring Hand, Journal of the Textile Machinery Society, 24(4), 58–68.
17.
RombaldoniF., DemichelisR., and MazzuchettiG.2011. Prediction of human psychophysical perception of fabric crispness and coolness hand from rapidly measurable low-stress mechanical and thermal parameters. Journal of Sensory Studies, 25 (2010) 899–916
18.
ElderH.M., FisherS., ArmstrongK., HutchisonG.1984. Fabric Softness, Handle and Compression, Journal of Textile Institute, 75, 37–46.
19.
DuboisD., PradeH.1997. Fuzzy criteria and fuzzy rules in subjective evaluation – a general discussion, Proceedings of EUFIT'97, Aachen, Germany, 975–979.
20.
KwakC., VenturaJ.A., Tofang-SaziK.2000. A neural network approach for defect identification and classification on leather fabric, Journal of Intelligent Manufacturing, 11, 485–499.
21.
ZengX., KoehlL., SahnounM., BuenoM.A., RennerM.2004. Integration of human knowledge and measured data for optimization of fabric hand, International Journal of General Systems33 (2–3), 243–258.
22.
HuiC.L., LauT.W., NgS.F.2004. Neural network prediction of human psychological perceptions of fabric hand, Textile Research Journal, 74(5), 375–383.
23.
El-Ghezal JeguirimS., Babay DhouibA., SahnounM., CheikhrouhouM., SchacherL., and AdolpheD.2011. The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics, Journal of Intelligent Manufacturing, Under press, DOI 10.1007/s10845-009-0362-y.
24.
AbdiH., and ValentinD.2007. Some new and easy ways to describe, compare, and evaluate products and assessors. The International Summer Program in Sensory Evaluation, SPISE2007, 25–26 July, 2007, Vietnam.
25.
ISO.1993. Sensory analysis: General guidance for the selection, training and monitoring of assessors. In ISO-standard 8586, The International Organization for Standardization, Geneva, Switzerland.
26.
El-GhezalS., Babay DhouibA., SahnounM., CheikhrouhouM., NjeugnaN., SchacherL.2008. Study of the tactile evaluation of knitted fabrics, Proceedings Fiber Society Spring Conference, 106–107.
27.
El-Ghezal JEGUIRIMS., SahnounM., Babay DhouibA., CheikhrouhouM., NjeugnaN., SchacherL., and AdolpheD.2009. The tactile sensory evaluation of knitted fabrics: effect of some finishing treatments, Journal of Sensory Studies25(2), 201–215.
28.
El-Ghezal JEGUIRIMS., Babay DhouibA., SahnounM., CheikhrouhouM., SchacherL., and AdolpheD.2010.b. The effect of knitting parameters on the sensory properties tactile feeling of knitted fabrics, Journal Tekstil, 59 (4), 122–135.
29.
PhilippeF., SchacherL., AdolpheD., and DacremontD.2004. Tactile Feeling: Sensory Analysis Applied to Textile Goods. Textile Research Journal, 74 (12), 1066–1072.
30.
AFNOR.1987. Textiles - Essais des tricots -Méthodes de relaxation et de mesure des variations dimensionnelles à la relaxation. In NF G07-102, French Association of Standardization.
31.
BS EN ISO 139:2005 (2005). International Organization for Standardization, Textiles.Standard atmospheres for conditioning and testing.
32.
HaykinS.2000. Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 2nd edition, 1999. Presented at the ASME ICE Division Fall 2000 Technical Meeting September 25–27, Peoria.
33.
RumelhartD. E., HintonG. E., WilliamsR. J.1986. Learning Representations by Back-propagating errors, Nature323, 533–536.
34.
ZadehL. A., 1965. Information and Control, 8, 338.
35.
ZimmermanH. J.1996. Fuzzy Set Theory and Its Applications, 2nd Ed., Allied Publishers Limited, New Delhi.
36.
ErtugrulS., UcarN.2000. Predicting Bursting Strength of Cotton Plain Knitted Fabrics Using Intelligent Techniques, Textile Research Journal, 70, 845–851.
37.
FukunagaK.1990. Introduction to Statistical Pattern Recognition, 2nd Ed. Academic1, San Diego, C.A. Mamdani, E.H., Assilian, S. 1975. An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies 7, 1–13.
