Journal of Computational Applied Mechanics

A two-phase hybrid product design algorithm using learning vector quantization, design of experiments, and adaptive neuro-fuzzy interface systems to optimize geometric form in view of customers’ opinions

Document Type : Research Paper

Authors

1 Engineering Graphics Center, Sharif University of Technology, Tehran, Iran

2 Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

3 Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada

Abstract

One of the most important characteristics of a modern product is the extent to which it meets the needs of customers to gain market share. The conceptual design methods of products based on customer requirements are often feature-based, in which several features are identified between different types of a product. According to customer demands, these features are tuned and the closest is selected as the optimum.
The great variety of features of a present-day product can often make this difficult because finding these common features is very complicated or even impossible. To solve this problem, choosing the optimal design is divided into two phases: In the first phase, the main product is divided into some basic categories and according to the customers' opinion, one is selected as the "winning category". In the second phase, the selection of common geometrical features between the members of the winning category is made. Then, the optimization process is done based on customer rating and the closest design to the mentioned rating is selected. The house light switch is used as a case study and the proposed algorithm is implemented on it.
High customer satisfaction with the optimized final design, high response rate to survey forms, and the low number of incompatible data, all, indicate the suitability of the proposed algorithm with human interface characteristics, simplicity and efficiency in adapting the product to the customers' view. This method can be used for other industrial products and even for non-industrial products or services.

Keywords

[1]        N. Williams, S. Azarm, P. K. Kannan, Engineering product design optimization for retail channel acceptance, Journal of Mechanical Design, Transactions of the ASME, Vol. 130, pp. 0614021-06140210, 2008.
[2]        Structural and Multidisciplinary Optimization, Title 34, Hybrid multi-objective shape design optimization using Taguchi's method and genetic algorithm, 2007, pp. 317-332.
[3]        H. Jiang, C. K. Kwong, Y. Liu, W. H. Ip, A methodology of integrating affective design with defining engineering specifications for product design, International Journal of Production Research, Vol. 53, pp. 2472-2488, 2015.
[4]        G. Taguchi, M. S. Phadke, Quality Engineering Through Design Optimization., 1984, pp. 1106-1113.
[5]        B. Agard, A. Kusiak, Data-mining-based methodology for the design of product families, International Journal of Production Research, Vol. 42, pp. 2955-2969, 2004.
[6]        S. K. Moon, S. R. T. Kumara, T. W. Simpson, Data mining and fuzzy clustering to support product family design, Proceedings of the ASME Design Engineering Technical Conference, Vol. 2006, pp. 1-9, 2006.
[7]        J. Wu, LVQ neural network based classification decision approach to mechanism type in conceptual design, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, pp. 378-384.
[8]        C. C. Yang, A classification-based Kansei engineering system for modeling consumers' affective responses and analyzing product form features, Expert Systems with Applications, Vol. 38, pp. 11382-11393, 2011.
[9]        K. Y. Chanb, C. K. Kwonga, T. S. Dillonb, K. Y. Funga, An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness, Journal of Engineering Design, Vol. 22, pp. 523-542, 2011.
[10]      L. H. Chen, W. C. Ko, Fuzzy approaches to quality function deployment for new product design, Fuzzy Sets and Systems, Vol. 160, pp. 2620-2639, 2009.
[11]      S. W. Hsiao, H. C. Tsai, Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design, International Journal of Industrial Ergonomics, Vol. 35, pp. 411-428, 2005.
[12]      J. C. H. Chung, D. R. Patel, R. L. Cook, M. K. Simmons, Feature-based modeling for mechanical design, Computers and Graphics, Vol. 14, pp. 189-199, 1990.
[13]      X. T. Cai, W. D. Li, Partial encryption of feature-based product models for collaborative development, Robotics and Computer-Integrated Manufacturing, Vol. 63, 2020.
[14]      S. Anand, Feature-based modelling approaches for integrated manufacturing: State-of-the-art survey and future research directions, International Journal of Computer Integrated Manufacturing, Vol. 8, pp. 411-440, 1995.
[15]      R. K. Li, B. W. Taur, H. J. Shyur, A two-stage feature-based design system, International Journal of Production Research, Vol. 29, pp. 133-154, 1991.
[16]      D. W. Rosen, Feature-based design: Four hypotheses for future CAD systems, Research in Engineering Design, Vol. 5, pp. 125-139, 1993.
[17]      H. T. Nguyen, S. Z. Md Dawal, Y. Nukman, H. Aoyama, K. Case, An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation, PLoS ONE, Vol. 10, pp. e0133599, 2015.
[18]      R. R. Yager, A procedure for ordering fuzzy subsets of the unit interval, Information Sciences, Vol. 24, pp. 143-161, 1981.
[19]      M. K. Bashar, N. Ohnishi, T. Matsumoto, Y. Takeuchi, H. Kudo, K. Agusa, Image retrieval by pattern categorization using wavelet domain perceptual features with LVQ neural network, Pattern Recognition Letters, Vol. 26, pp. 2315-2335, 2005.
[20]      J. Liu, B. Zuo, X. Zeng, P. Vroman, B. Rabenasolo, Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network, Expert Systems with Applications, Vol. 37, pp. 2241-2246, 2010.
[21]      A. K. Qin, P. N. Suganthan, Initialization insensitive LVQ algorithm based on cost-function adaptation, Pattern Recognition, Vol. 38, pp. 773-776, 2005.
[22]      G. Taguchi, Quality engineering in Japan, Communications in Statistics - Theory and Methods, Vol. 14, pp. 2785-2801, 1985.
[23]      M. Gen, R. Cheng, Survey of penalty techniques in genetic algorithms, Proceedings of the IEEE Conference on Evolutionary Computation, IEEE, 1996, pp. 804-809.
[24]      F. Herrera, M. Lozano, J. L. Verdegay, Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis, Artificial Intelligence Review, Vol. 12, pp. 265-319, 1998.
[25]      E. Ziegel, Genetic Algorithms and Engineering Optimization, Technometrics, Vol. 44, pp. 95-95, 2002.
[26]      D. W. Zingg, M. Nemec, T. H. Pulliam, A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization, European Journal of Computational Mechanics, Vol. 17, pp. 103-126, 2008.
[27]      M. P. Thompson, J. D. Hamann, J. Sessions, Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems, International Journal of Forestry Research, Vol. 2009, pp. 1-14, 2009.
Journal of Computational Applied Mechanics
Volume 52, Issue 2
June 2021
Pages 271-296
  • Receive Date: 28 March 2020
  • Revise Date: 26 June 2020
  • Accept Date: 19 July 2020