Kansei Engineering-based Research On Form Design Of Mobile Phones

As the development of information industry, mobile phones gradually become the necessity. During the process of purchasing mobile phones, the form design which can bring aesthetic feeling to consumers is the dominated factor except for functions and price.There is a misunderstanding between designer and consumer. How to identify consumer’s feeling preference property and conformity this property into products are still big problem for designers. Therefore, how to design products that meet consumers’ satisfaction is a common problem for all the world’s manufacturers.There are two main challenges on the research of Kansei Engineering:Firstly, how to deal with consecutive variables; Secondly, the optimization of form design for products.This paper provide ways to solve the questions. A way combining size definition and categories to demonstrate a kind of product is provided. After the optimal form design model constructed, a methodology integrating Neural Network and Genetic Algorithm is given to compute the model and get the optimal result.This thesis mainly finishes the following tasks:(1)Based on market investigation and questinnairs analysis, design variables and its value scope are obtained through a way combining size definition and categories. An experienced designer was invited to design40mobile phone models with PROE software based on design requirements.(2)123D mobile phone models were selected through multi-dimensional scale analysis, cluster analysis. Based on the former research, a questionnaire was constructed and sented to a group of people between18and30.(3)Consumers’ preference model and the model between feeling preference and design variables were built respectively with the Neural Network methods. These two types of structural equation modeling reflected the relations among image sense words, design variables and the overall preference. The first model can predict overall preference with input of image sense words’ value. The second model can predict image sense value and overall preference with input of design variables’ value.(4)Considering design requirements and consumer’s preference, the form optimization model is established. (5)Combining Genetic Algorithm and Neural Network, this paper provide a way to solve the form optimization model and finally the approximated optimal result was obtained. The former expert design a new model with the result.Validation indicated the models and algorithm were effective.The studies on form in this paper solved the two difficult challenges in Kansei Engineering. Meanwhile, the results of this research can help designers grasp the consumers’real preferences in product design process and design affective products.