Optimization Design Of Front Face Based On Kesei Engineering

With cars gradually become people’s increasingly favored means of transport, the constraints of car appearances have been gradually removed by their mature function. Besides, the function is no longer what the consumers only care about, they also pay more attention to the sensory experience caused by appearance when buying a car. But it’s difficult for designers to identify the buyers’ Kansei Preferences property and convert them into products, buyers are not always satisfied with what the designers design. So how to design products that meet the buyers needs is the key to success.The main research contents of this thesis are as follows:(1) Conducting eye movement experiments with eye tracking technology, analyzing the hot spot map to extract the key shape characteristics of car faces.(2) Collecting and screening the Kansei appraisal words for the evaluation of front face through the web, magazines and literature.(3) Using the quantitative Ⅰclass and coordinates to explain the front face design variables. Drawing sample pictures of32cars through adjusting the variable range and referring to the actual design parameters.(4) Using multi-scale analysis and cluster analysis to classify the shape samples and find out the representative ones, designing the questionnaire, and establishing rating scale of the front face (5) Establishing the relationship model between consumers’ Kansei preference and Kansei words with BP neural network, validating the model.(6) Establishing the relationship model between consumers’ Kansei preference and design variableswith BP neural networ, validating the model.(7) Using the genetic algorithm to build optimization model of car front face, according to design requirements and preferences.The results of this thesis can help the designers understand the consumer preferences and convert them into products.