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User-oriented design for the optimal combination on product design
Authors:Hsin-Hsi Lai  Yang-Cheng Lin  Chung-Hsing Yeh  Chien-Hung Wei
Institution:aDepartment of Industrial Design, National Cheng Kung University, Tainan 701, Taiwan;bSchool of Business Systems, Monash University, Clayton, Victoria 3800, Australia;cDepartment of Transportation and Communication Management Science, National Cheng Kung University, Tainan, 701 Taiwan
Abstract:This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product color affect product image individually and as a whole. The concept of Kansei Engineering is used to extract the experimental samples as a data base for Quantitative Theory Type I and neural networks (NNs). The result of numerical analysis suggests that mobile phone makers need to provide various product colors to attract users, in addition to product forms. This paper demonstrates the advantage of using NNs for determining the optimal combination of product form and product color, particularly if the product into design elements. Based on the analysis of NNs, we can use 72 representative product colors of each mobile phone to develop a product color data base consisting of 16777216 (=256×256×256, True-Color model) colors with the associated product image. The design data base provides useful insights to save any amount of money and time for the new product development. The product designers can input a product image to work out an adequate color on a mobile phone. Furthermore, the design data base can be used, in conjunction with computer-aided design system or virtual reality technology, to build a 3D model for facilitating the design process of mobile phones. Although, the mobile phones are chosen as the object of the experimental study, this approach can be applied to other products with various design elements.
Keywords:User-oriented design  Product design  Perception  Kansei engineering  Neural networks
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