A neural network-based computer aided design tool for automotive form design

Although the functionality and performance are both important aspects in vehicle design, an automotive form is a crucial factor in determining a consumer's image perception and purchase decision. Hence, an effective tool for designing successful automotive form was suggested. Based on Kansei Engineering principles, the relationship between the profile characteristics and the consumer's image perception is established using a Back-Propagation Neural (BPN) network. A Computer Aided Design (CAD) tool, which uses the trained BPN to predict the consumer perception of an automotive profile expressed in the form of a numerical definition, is constructed using Visual Basic software. The performance of the CAD prototype tool is verified by comparing its predictions to the actual consumer perception evaluations. A good similarity is identified between the two sets of results. Therefore, the developed tool provides designers with powerful means of creating automotive designs from a consumer's image perception perspective.