TY - GEN
T1 - Style design method based on form impressions
AU - Nordgren, Andreas
AU - Aoyama, Hideki
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Computer Aided Design (CAD)
KW - Factor Analysis
KW - Form Impressions
KW - Neural Networks
KW - Style Design
UR - http://www.scopus.com/inward/record.url?scp=33144473895&partnerID=8YFLogxK
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U2 - 10.1115/detc2005-84955
DO - 10.1115/detc2005-84955
M3 - Conference contribution
AN - SCOPUS:33144473895
SN - 079184739X
SN - 9780791847398
T3 - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - DETC2005
SP - 5
EP - 12
BT - Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conferences - DETC2005
PB - American Society of Mechanical Engineers
T2 - DETC2005: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Y2 - 24 September 2005 through 28 September 2005
ER -