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Style Design Method Based on Form Impressions

[+] Author Affiliations
Andreas Nordgren, Hideki Aoyama

Keio University, Yokohama, Japan

Paper No. DETC2005-84955, pp. 5-12; 8 pages
doi:10.1115/DETC2005-84955
From:
  • ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 31st Design Automation Conference, Parts A and B
  • Long Beach, California, USA, September 24–28, 2005
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4739-X | eISBN: 0-7918-3766-1
  • Copyright © 2005 by ASME

abstract

The automotive industry is very competitive and companies are spending enormous amounts of resources on the development of new cars. The success of a new model is highly correlated to how well the designers and engineers have been able to blend features, functionality, quality and design to bring an attractive car to a certain segment of the market at the right time. Furthermore, as modern manufacturing techniques have enabled most manufacturers to offer standard features in their cars, the design has become a major selling point and one of the key factors for the ‘image’ associated with a company. However, the image, or form impression of a car, stated in natural language, is subtle and difficult to directly relate to concrete design parameters. With few tools to address this issue, designers are left to rely on their experience and sensitivity to current trends in order to meet the customer expectations for a new model. The purpose of the method reported in this paper is to provide a foundation for a design support system, which can help designers visualize and validate the complex relationship between form impressions and design parameters. This was achieved by expressing form impressions in natural language as sets of 10 weighted attributes. 14 design parameters were established to describe the basic shape of a car and data on the form impression for 31 different shapes was collected via a survey designed by the Taguchi method. Factor analysis was performed to extract correlated factors and eliminate the overlap of meaning between attributes. A neural network, able to relate form impressions expressed in these factors to basic proportions of a car, was created, trained and used to generalize design parameters corresponding to any form impression presented to it. Finally, a 3D-model with the desired form impression was automatically created by the CAD-system outlined in this paper. These results show that this method can be used to create a design support system, which has a sensibility to the form impressions various shapes will give.

Copyright © 2005 by ASME

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