A hybrid Kansei engineering design expert system based on grey system theory and support vector regression

Nowadays customers choose products strictly in terms of their specific demands. How to quickly and accurately catch customers’ feelings and transform them into design elements and vice versa becomes an important issue. This study explores the bi-directional relationship between customers’ demands or needs and product forms by using a novel integral approach. High-price machine tools are used as our demonstration target. This integral approach adopts the “grey system theory (GST)”, and the state-of-the-art machine learning based modeling formalism “support vector regression (SVR)” in the “Kansei engineering (KE)” process. The GST is used to effectively determine the influence weighting of form parameters on product images and the SVR is used to precisely establish the mapping relationship between product form elements and product images. Furthermore, for practical concerns, a user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes.

Research highlights
► The bi-directional relationship between customers’ demands or needs and product forms was explored by using a novel integral approach.
► This integral approach adopts the “grey system theory (GST)”, and the state-of-the-art machine learning based modeling formalism “support vector regression (SVR)” in the “Kansei engineering (KE)” process.
► A user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes.