Online from: 1989
Subject Area: Mechanical & Materials Engineering
|Title:||Analysis of tactile perceptions of textile materials using artificial intelligence techniques: Part 2: reverse engineering using genetic algorithm coupled neural network|
|Author(s):||B. Karthikeyan, (School of Engineering and Textiles, Philadelphia University, Philadelphia, Pennsylvania, USA Department of Electrical and Computer Sciences, College of Engineering, Temple University, Philadelphia, Pennsylvania, USA), Les M. Sztandera, (School of Business Administration, Philadelphia University, Philadelphia, Pennsylvania, USA)|
|Citation:||B. Karthikeyan, Les M. Sztandera, (2010) "Analysis of tactile perceptions of textile materials using artificial intelligence techniques: Part 2: reverse engineering using genetic algorithm coupled neural network", International Journal of Clothing Science and Technology, Vol. 22 Iss: 2/3, pp.202 - 210|
|Keywords:||Artificial intelligence, Mechanical properties of materials, Modelling, Programming and algorithm theory, Textiles|
|Article type:||Research paper|
|DOI:||10.1108/09556221011018667 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The research reported was supported in whole by Laboratory for Engineered Human Protection through Grant W911QY-04-0001 from Department of Defense/US Army Natick Soldier Systems Center. The authors would like to acknowledge Contracting Officer Technical Representative Carole Winterhalter, Warfighter Science, Technology and Applied Research Directorate, US Army Natick Soldier Research, Development and Engineering Center, Natick, Massachusetts. The authors also thank Dr Howard Schutz, University of California-Davis, Davis, California, and Visiting Scientist at Natick, Massachusetts, and Dr Armand Cardello, Senior Research Scientist, Natick, Massachusetts.|
Purpose – The second of a two-part series, this paper aims to explain the design and development of a hybrid system for reverse engineering.
Design/methodology/approach – A prediction engine to map the perception of tactile sensations using a neural network engine was developed. Since seventeen mechanical properties form the input - and tactile compfort score is used as the output - a direct reversal of the data set becomes impossible, hence, a hybrid approach was employed. The neural net is coupled with a genetic algorithm engine for the reversal process. The trained neural network acts as the objective function to evaluate the property set while the solution set is generated by Genetic Algorithm (GA) engine. Limitation of the GA and a means to overcome it is discussed. Application software based on the current research is also presented.
Findings – Human perception of tactile sensations is non-linear in terms of the mechanical properties of textile materials.
Originality/value – The paper deals with reverse engineering and discusses application software based on the current research.
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