Multi-dimensional Variable Optimization Design Of Products Based On Kansei Engineering

Consumers are more looking forward to the products with feelings, especially affordable digital products such as card camera. Rapid development of technology has made digital products function homogeneity. In this case, the manufacturers pay attention to the emotional needs of the consumers on product when they develop a new product. Emotions are decided jointly by product modelling, colour and material, so the multi-dimensional design variables for the product emotion design were studied and the following jobs were done:First, Multi-dimensional Scaling Analysis and Cluster Analysis were used for the Kansei image words screening, Kansei image words with the same meaning were classified into one group which is used to represent an aspect of the product. Second, the card camera’s global design variables were extracted through HIEs deconstruction method, then Partial Least Squares Analysis were used to extract the main design variables. Third, camera 3d models were drawn by rhino software. Finally, the relationship model among Kansei image words, Kansei preferences and the design variables was build by Neural Network and optimized by Genetic Algorithm.Grouping the similar Kansei image words for classification which could reduce the number of words, at the same time also could fully describe the perceptual demand for products. Then the comprehensive design variables were deconstructed and the main design variables were extracted.In this way the multi-dimensional design variables affecting consumer emotion were considered, at the same time the number of variables was effectively controlled.