Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.

[1]  Chih-Chieh Yang,et al.  A support vector regression based prediction model of affective responses for product form design , 2010, Comput. Ind. Eng..

[2]  한성호,et al.  Identifying mobile phone design features critical to user satisfaction , 2001 .

[3]  Brian J. Ross,et al.  Procedural 3D texture synthesis using genetic programming , 2004, Comput. Graph..

[4]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[5]  Patrick R. McMullen,et al.  Optimal product design using a colony of virtual ants , 2007, Eur. J. Oper. Res..

[6]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[7]  Ling Wang,et al.  A hybrid genetic algorithm-neural network strategy for simulation optimization , 2005, Appl. Math. Comput..

[8]  Shih-Wen Hsiao,et al.  Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design , 2005 .

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Sung H. Han,et al.  Optimal balancing of multiple affective satisfaction dimensions: A case study on mobile phones , 2008 .

[11]  J. M. Kittross.  The measurement of meaning , 1959 .

[12]  Shih-Wen Hsiao,et al.  A neurofuzzy-evolutionary approach for product design , 2004, Integr. Comput. Aided Eng..

[13]  Barton C. Massey,et al.  DESIGN METHODS , 2002 .

[14]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[15]  Mitsuo Nagamachi,et al.  Development of a design support system for office chairs using 3-D graphics , 1995 .

[16]  Wen-Yau Liang,et al.  A hybrid approach to constrained evolutionary computing: Case of product synthesis , 2008 .

[17]  Chih-Chieh Yang,et al.  Multiclass SVM-RFE for product form feature selection , 2008, Expert Syst. Appl..

[18]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[19]  Santosh K. Gupta,et al.  Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator , 2003, Comput. Chem. Eng..

[20]  Jyh-Rong Chou,et al.  Automatic design support and image evaluation of two-coloured products using colour association and colour harmony scales and genetic algorithm , 2007, Comput. Aided Des..

[21]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[22]  David B. MacKay,et al.  Chemometrics, econometrics, psychometrics—How best to handle hedonics? , 2006 .

[23]  Yukihiro Matsubara,et al.  Hybrid kansei engineering system and design support , 1997 .

[24]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[25]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[26]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[27]  Tao Yu,et al.  Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process , 2007, Eng. Appl. Artif. Intell..

[28]  Shih-Wen Hsiao,et al.  A neural network based approach for product form design , 2002 .

[29]  Nikolaos F. Matsatsinis,et al.  Particle Swarm Optimization for Optimal Product Line Design , 2009 .

[30]  Chih-Chieh Yang,et al.  Classification model for product form design using fuzzy support vector machines , 2008, Comput. Ind. Eng..