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Baby Cradle Design with Kansei Knowledge Mining Based on Rough Set Theory

  • Esra Akgül
  • Mihrimah Özmen
  • Emel Kizilkaya AydoğanEmail author
  • Cem Sinanoğlu
Chapter
  • 17 Downloads
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 279)

Abstract

Today, producing consumer-oriented products are a key to commercial success. The successful companies have to aim to capture consumer’s psychological perception. Kansei Engineering is a type of methodology to determine consumer’s perception on product elements for using new product development. Kansei evaluation dataset were significantly developed such as uncertainty reason techniques including rough set, fuzzy set and neural networks. In literature, the rough set theory has been widely used approach to reduce the complexity of the knowledge database. In this chapter, to extract more accurate design parameters and Kansei knowledge, rough set rule based mining was applied for Kansei engineering research to baby cradle design. The proposed design methodology results show that obtained rules are consistent with customer expectations.

References

  1. 1.
    Lu, W., Petiot, J.F.: Affective design of products using an audio-based protocol: application to eyeglass frame. Int. J. Ind. Ergon. 44(3), 383–394 (2014)CrossRefGoogle Scholar
  2. 2.
    Petiot, J.F., Yannou, B.: Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics. Int. J. Ind. Ergon 33(6), 507–525 (2004)CrossRefGoogle Scholar
  3. 3.
    Kano, N., Seraku, N., Takahashi, F., Tsuji, S.: Attractive quality and must-be quality. J Jpn. Soc. Qual. Cont. 41, 39–48 (1984)Google Scholar
  4. 4.
    Cohen, L.: Quality Function Deployment: How to Make QFD Work for You. Prentice Hall (1995)Google Scholar
  5. 5.
    Luce, R.D., Turkey, J.W.: Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psych. 1(1), 1–27 (1964)Google Scholar
  6. 6.
    Schütte, S., Eklund, J.: Design of rocker switches for work-vehicles—an application of Kansei engineering. Appl. Ergon. 36(5), 557–567 (2005)CrossRefGoogle Scholar
  7. 7.
    Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15(1), 3–11 (1995)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Nagamachi, M., Tachikawa, M., Imanishi, N., Ishizawa, T., Yano, S.: A successful statistical procedure on Kansei engineering products. In: 11th QMOD Conference. Quality Management and Organizational Development Attaining Sustainability from Organizational Excellence to Sustainable Excellence; 20–22 August; 2008 in Helsingborg; Sweden (No. 033, pp. 987–995). Linköping University Electronic Press (2008)Google Scholar
  9. 9.
    Tama, I.P., Azlia, W., Hardiningtyas, D.: Development of customer oriented product design using Kansei engineering and Kano model: Case study of ceramic souvenir. Procedia Man. 4, 328–335 (2015)CrossRefGoogle Scholar
  10. 10.
    Häfner, P., Ommeln, M., Katičić, J., Ovtcharova, J.: Immersive Kansei Engineering: A New Method and Its Potentials. Karlsruher Institut für Technologie (2018)Google Scholar
  11. 11.
    Lokman, A.M.: Design & emotion: the Kansei engineering methodology. Malays. J. Comp. 1(1), 1–11 (2010)Google Scholar
  12. 12.
    Morı, N.: Rough set approach to product design solution for the purposed Kansei. Bull. Japan. Soc. Sci. Des. 48(6), 85–94 (2002)Google Scholar
  13. 13.
    Nishino, T., Nagamachi, M., Sakawa, M.: Acquisition of kansei decision rules of coffee flavor using rough set method. Kansei Eng. Int. 5(4), 41–50 (2006)CrossRefGoogle Scholar
  14. 14.
    Nishino, T., Satsuta, R., Uematsu, M., Sugihara, S., Nagamachi, M.: Identification of customers’ latent Kansei needs and product design by rough set based approach. In: 11th QMOD Conference. Quality Management and Organizational Development Attaining Sustainability from Organizational Excellence to Sustainable Excellence; Linköping University Electronic Press, vol. 033 Sweden, Helsingborg, pp. 341–350 (2008)Google Scholar
  15. 15.
    Zhou, F., Jiao, R.J., Schaefer, D., Chen, S.: Rough set based rule mining for affective design. In: DS 58-9: Proceedings of ICED 09, the 17th international conference on engineering design, vol. 9, Human Behavior in Design, Palo Alto, CA, USA, pp. 245–254 (2009)Google Scholar
  16. 16.
    Shieh, M.D., Yeh, Y.E., Huang, C.L.: Eliciting design knowledge from affective responses using rough sets and Kansei engineering system. J Ambient. Intell. Hum.Ized Comp. 7(1), 107–120 (2016)CrossRefGoogle Scholar
  17. 17.
    Pawlak, Z.: Rough sets. Int. J. Comp. Inform. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  18. 18.
    Predki, B., Slowinski, R., Stefanowski, J., Susmaga, R., Wilk, S.Z.: ROSE—software implementation of the rough set theory. In: Polkowski, L., Skowron, A. (eds.) Rough design of waiting areas 407 Sets and Current Trends in Compter: Lecture Notes in Artificial Intelligence, vol. 1424, Springer, Berlin (1998)Google Scholar
  19. 19.
    Sabu, M.K., Raju, G.: Rule induction using Rough Set Theory—an application in agriculture. In: 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). IEEE, pp. 45–49 (2011)Google Scholar
  20. 20.
    Aydogan, E.K., Ozmen, M., Delice, Y.: CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems. Neural Comp. App. 1–19 (2018)Google Scholar
  21. 21.
    Schütte, S. (2002). Designing feelings into products: integrating Kansei engineering methodology in product development. Thesis, Department Mechanical Engineering, Linkopings University, Linkoping, SwedenGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Esra Akgül
    • 1
  • Mihrimah Özmen
    • 2
  • Emel Kizilkaya Aydoğan
    • 2
    Email author
  • Cem Sinanoğlu
    • 1
  1. 1.Department of Industrial Design EngineeringErciyes UniversityKayseriTurkey
  2. 2.Department of Industrial EngineeringErciyes UniversityKayseriTurkey

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