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A Study of Kansei Engineering in PET Bottle Silhouette

[+] Author Affiliations
Khusnun Widiyati, Hideki Aoyama

Keio University, Yokohama, Kanagawa, Japan

Paper No. DETC2011-48066, pp. 849-858; 10 pages
doi:10.1115/DETC2011-48066
From:
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5479-2
  • Copyright © 2011 by ASME

abstract

Today’s customers see the product not only based on its functionality and its value, but also based on its aesthetic view. And from day to day, the level of consumers’ aesthetic satisfaction is advanced. Companies have to struggle in recognizing and feeding the customers with higher and higher level of aesthetics design, in order to win the competition. Kansei Engineering is a powerful tool during product designs that analyze the design in relation with consumers’ feeling toward the product. In this saturated market, moreover, the application of Kansei Engineering might be a way out to provide a product emotionally attached to the customers’ feeling. PET bottles, particularly bottles distributed in Japan, is an example of “everyday-beverage-container” which has many variations in the shape. Among the many product attributes, product form/shape is one of the product attribute that can attract emotional attachment to the customer. In this paper, physical attributes of PET bottle which evoke consumers to have certain emotional attachment were evaluated using Kansei Engineering. In order to do this, 18 models of PET bottle generated using Taguchi Methods, and 9 emotional evaluation words were applied in a questionnaire. By using Taguchi Methods, important shape parameters that evoke customer to certain emotional feeling were identified. Validation to the Taguchi Methods’ finding was validated using Artificial Neural Network (ANN). The validation was performed by mapping the Kansei/emotional space to shape parameter space. Evaluation towards the result from Taguchi Methods and ANN was performed. Comparison between Taguchi Methods’ and ANN’s result showed that both result were correlated.

Copyright © 2011 by ASME

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