In the process of product form design, the quantitative design method of combining the user-oriented Kansei images and product form design elements should be developed more objectively and logically. In order to satisfy the user perception of the Kansei image, this paper presents a new approach of the product form design based on Gray Relational Analysis (GRA) fuzzy theory. Firstly, GRA-Fuzzy logic model is used to effectively identify the form elements that influence the Kansei images. Secondly, according to the membership function that the input and output linguistic variables are determined based on the fuzzy theory, the fuzzy rules that associate product form elements with Kansei images are constructed. Thirdly, GRA-Fuzzy logic model is built to analyze and evaluate the product form design. The train seat form design is used as a case study. The result shows that the model is effective in predicting the product Kansei images, and it improves and optimizes the product form design process. Furthermore, GRA-Fuzzy logic model can be used, in conjunction with computer aided design system or X Reality technology, to build a 3-D model for promoting the design process of products.

The quantitative relationship between sex- and age-specific height and real per capita income is tested in this paper for the case of France for the period 1815-1986, and for the case of Italy for the years 1861-1973.1 Such a model was first developed by Brinkman, Drukker and Slot in 1985 on the basis of data on the heights of Dutch conscripts in the first decennia of the twentieth century. 2 It is demonstrated that the original model, derived from time-series covering less than half a century, still holds when it is tested against yearly data of two different countries, covering a timespan of more than a century and a half. The results of the research, summarized in this pape~ suggest that data on height can be used as estimators for changes in material well-being over time. Furthermore, timeseries of real per capita income can be used to fill in gaps where information on the development of height is missing.