Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28162
Title: Product form design using customer perception evaluation by a combined superellipse fitting and ANN approach
Authors: Tang, CY 
Fung, KY
Lee, EWM
Ho, GTS
Siu, KWM
Mou, WL
Keywords: Affective design
Artificial neural network
Product aesthetics
Product form
Superellipse fitting
Issue Date: 2013
Publisher: Elsevier Sci Ltd
Journal: Advanced Engineering Informatics 
Abstract: Product aesthetics plays an important role in new product design and development. Product form can deliver product images and affect customer's impression to a product. However, it is usually difficult to apply conventional approaches to represent the product form precisely and effectively for modeling the relationship between product image and customer perception. The objective of this work is to develop a computational technique for product aesthetics design so that customer perception can be taken into product form design in a more systematic and intelligent manner. To achieve this aim, a novel parametric approach is proposed to introduce design parameters such as line, size, and ratio into product design model and the technique of generalized superellipse fitting is adopted to describe the outline pattern of a product. Since customer perception on a product is highly non-linear and very difficult to be described by any traditional mathematical approaches, an artificial neural network (ANN) model is therefore established to relate the design parameters and the perceptual values for the design of a new product. A case study of mobile phone design, in which twelve numerical parameters are defined for the conceptual model, has been conducted to explain the implementation of the proposed approach. A three-layered perceptron ANN model is developed to predict the perceptual values of stylishness based on a survey using 32 mobile phone samples. The results of the case study illustrate that the proposed approach can successfully generate an optimum design of a mobile phone by applying a genetic algorithm (GA) on the trained ANN model.
URI: http://hdl.handle.net/10397/28162
ISSN: 1474-0346
DOI: 10.1016/j.aei.2013.03.006
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