Multi-objective aesthetic design optimization for minimizing the effect of variation in customer kansei

Abstract

Due to maturation of science and technology, companies are required to differentiate their products in terms of subjective qualities such as aesthetics whose evaluation depends on customer kansei instead of objective qualities such as performance, function and cost. In the field of kansei engineering, various methods that design products aesthetics fitted to customer kansei have been researched, but diversity of customers’ kansei is becoming a big issue in those researchs. Industrial products are in general designed and sold for a huge number of customers, not a single customer. Due to diversity of their kansei, that they may receive completely different impressions or have different preferences from the same product. As a result, it becomes quite difficult to design products that satisfy all customers. To overcome such difficulty, this paper proposes a new method for designing product aesthetics that give the same / similar impressions to all customers even if their kansei is diverse. To achieve this, the proposed method is based on the concept of robust design. In particular, customers evaluate existing products in questionnaire investigation. Response surfaces that approximately represent the relationships between customer’s impressions received from existing products and their aesthetic elements are then calculated for each customer. Sum of squares of the difference between target impression scores and estimated ones of a design candidate is calculated for each customer as a utility. Optimal design parameters that minimize both mean and variance of all customers’ utility are finally explored by using multi-objective genetic algorithm. In the case study, the proposed method was applied to artificially generated questionnaire results in which the variation in customer kansei is quantitatively controlled. The results revealed that the proposed method can design products that keep low the effect of the variation in customer kansei while achieving a design goal.