# Research on Optimized Product Image Design Integrated Decision System Based on Kansei Engineering
## 4 Case Study of Train Seat Image Design
### Train seat Image Design Quantitative Decision Model  
Based on the conclusion of train seat image design qualitative decision model, the design elements that have great influence on seat image are seat form and color design elements. Using the experimental process of Kansei Engineering, the GRA-Fuzzy logic sub-models of train seat form design and color design are respectively constructed. Based on the GRA-Fuzzy theory, this paper analyzes the form sub-elements and the color sub-elements. Aimed at the representative Kansei words of the seat image, the subjects evaluate the Kansei image of seat samples through the seven-point Likert scale to build up the train seat image database. As the result of design element analysis, the 9 form sub-elements and their corresponding element types are extracted from the 30 representative train seat samples through morphological analysis. In this section, we select one representative train seat randomly from the 30 representative train seat samples and fill the selected train seat with 72 representative colors as the experimental train seat color samples, for a total of 72 color samples. In Table 3, Column 1 and 7 display the numbers of color samples, and the remaining columns display the corresponding color sub-element (\"Hue (\(h^{\circ}\))\" element (\(x_{\rm c1}\)), \"Chroma (C\({}^{\circ}\))\" element (\(x_{\rm c2}\)), \"Lightness (\(L^{\star}\))\" element (\(x_{\rm c3}\)), \"parameter \(a^{\star\prime}\)\" element (\(x_{\rm c4}\)) and \"parameter \(b^{\star\prime}\)\" element (\(x_{\rm c5}\))) values of these color samples. The GRA method is used to obtain the grey correlation degree of form design sub-element as shown in Table 4 from Section 3.2.1, and the sub-elements of form design that affect the Kansei image  
Figure 11: The qualitative decision model of train seat image design. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No.** & \(h^{\circ}\) & \(C^{\bullet}\) & \(L^{\bullet}\) & \(a^{\bullet}\) & \(b^{\bullet}\) & **No.** & \(h^{\circ}\) & \(C^{\bullet}\) & \(L^{\bullet}\) & \(a^{\bullet}\) & \(b^{\bullet}\) \\ \hline C22 & 90 & 60 & 50 & 0 & 60 & C28 & 135 & 60 & 30 & \(-\)42 & 42 \\ C23 & 90 & 40 & 50 & 0 & 40 & C29 & 135 & 40 & 30 & \(-\)28 & 28 \\ C24 & 90 & 20 & 50 & 0 & 20 & C30 & 135 & 20 & 30 & \(-\)14 & 14 \\ C25 & 90 & 60 & 70 & 0 & 60 & C31 & 135 & 60 & 50 & \(-\)42 & 42 \\ C26 & 90 & 40 & 70 & 0 & 40 & C32 & 135 & 40 & 50 & \(-\)28 & 28 \\ C27 & 90 & 20 & 70 & 0 & 20 & C33 & 135 & 20 & 50 & \(-\)14 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Color element values of the representative train seat color sample C22–C33. of train seat are, respectively, \"Seat back waistline\" (\(x_{5}\)), \"Seat back body shape\" (\(x_{4}\)), \"Headrest shape\" (\(x_{2}\)), \"Length ratio between seat-back to seat\" (\(x_{8}\)), \"Relationship between seat-back and headrest\" (\(x_{3}\)) and \"Seat-back slope angle\" (\(x_{9}\)). The above form design sub-elements that have greater influence on Kansei image are taken as the input linguistic variables and the Kansei image of train seat as output linguistic variables. The GRA-Fuzzy logic sub-model between the form design sub-elements and Kansei image is constructed by the triangular membership function. Afterwards, the corresponding 54 fuzzy rules are built up, and the GRA-Fuzzy logic sub-model of train seat form design is constructed. The fuzzy rules No. 22-No. 30 have been added as followed in Table 5.  
According to Section 3.2.1, GRA method is used to obtain the grey correlation degree of color design sub-element. It shows that the color design sub-elements that affect the Kansei image of train seat are \"Lightness (\(L\))\" (\(x_{5}\)), \"Chroma (C)\" (\(x_{2}\)), \"parameter \(a\)\" (\(x_{4}\)) and \"parameter \(b\)\" (\(x_{5}\)).