Analysis of tactile perceptions of textile materials using artificial intelligence techniques: Part 1: forward engineering

Purpose – The first of a two‐part series, this paper aims to discuss the design and development of an artificial intelligence‐based hybrid model to understand human perception of the tactile properties of textile materials and create an objective system to express those tactile perceptions in terms of measurable mechanical properties. Design/methodology/approach – A forward engineering system using the Model Free Algorithm approach of the Artificial Intelligence Technique to predict the tactile comfort score is presented. Findings – Human perception of tactile sensation is based on the weighted stimulus perceived by the human neural system. Originality/value – Contribution to intelligent textile and garment manufacture.

[1]  Les M. Sztandera Predicting tactile fabric comfort from mechanical and handfeel properties using regression analysis , 2008 .

[2]  Armand V. Cardello,et al.  Predicting the Handle and Comfort of Military Clothing Fabrics from Sensory and Instrumental Data: Development and Application of New Psychophysical Methods , 2003 .

[3]  Jinlian Hu,et al.  Structure and Mechanics of Woven Fabrics , 2004 .

[4]  Les M. Sztandera Identification of the most significant sensory and mechanical properties influencing tactile fabric comfort , 2008 .

[5]  Roger L. Barker,et al.  The Bending Behaviour of Plain-woven Fabrics Part II: The Case of Linear Thread-bending Behaviour , 1990 .

[6]  George K Stylios Mechatronic Principles for Aesthetic Measurement of Textile Materials , 1998 .

[7]  J. Amirbayat The Buckling of Flexible Sheets under Tension Part I: Theoretical Analysis , 1991 .

[8]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[9]  George K Stylios,et al.  An investigation into the engineering of the drapability of fabric , 2002 .

[10]  R. Postle,et al.  Objective specification of fabric quality, mechanical properties and performance , 1982 .

[11]  B. Karthikeyan,et al.  Artificial Neural Network-embedded Expert System for the Design of Canopy Fabrics , 2006 .

[12]  R. Postle,et al.  40—AN ENERGY ANALYSIS OF WOVEN-FABRIC MECHANICS BY MEANS OF OPTIMAL-CONTROL THEORY PART II: PURE-BENDING PROPERTIES , 1977 .

[13]  D. R. Baughman,et al.  Neural Networks in Bioprocessing and Chemical Engineering , 1992 .

[14]  H. M. Behery,et al.  Effect of mechanical and physical properties on fabric hand , 2005 .

[15]  Yi Li,et al.  Predicting Clothing Sensory Comfort with Artificial Intelligence Hybrid Models , 2004 .

[16]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[17]  Yi Li,et al.  Neural Network Predictions of Human Psychological Perceptions of Clothing Sensory Comfort , 2003 .

[18]  Roger L. Barker,et al.  The Bending Behaviour of Plain-woven Fabrics Part I: A Critical Review , 1990 .