Rule-based inference model for the Kansei Engineering system

Kansei Engineering has been applied to product development for customer satisfaction based on ergonomic technology. The system is composed of three parts such as Kansei analysis, inference mechanism, and presentation technologies. The inference mechanism by which human Kansei is translated into design elements plays an important role in Kansei Engineering. The reasoning logic in the system must satisfy several conditions. First, the whole aspects of design elements must be considered in the reasoning processes. Second, the reasoning logic can make the discrimination between Kansei words selected by customers and other words. Third, the reasoned results must have reliability high enough to reduce the difference between customer's image and reasoned design elements. In this paper, we propose a rule-based inference model which will cover the above-mentioned conditions. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected Kansei words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.