Development of a Parametric Kansei Design Procedure

• Main Authors: Hung-Yuan Chen, 陳鴻源
• Other Authors: Yu-Ming Chang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/48731729058063526604

博士 === 國立成功大學 === 工業設計學系碩博士班 === 96 === Consumers interact with a vast number of diverse products during the course of their daily lives and therefore subconsciously develop powerful product evaluation and discrimination skills. A consumer’s psychological perception of a product is significantly influenced by its aesthetics, and thus product form plays an essential role in determining the commercial success of a product. Intuitively, it seems reasonable to suppose that a consumer’s psychological satisfaction with a product is governed by a particular sub-set of the product’s form features. Consequently, it is of fundamental interest to designers to identify these critical form features during the early stages of the design process such that a product design can be produced which more closely matches the product image expectations of the target consumers. he evolution of a product’s form during the design process is typically governed by the designer’s individual preferences and creative instincts. As a consequence, there is a risk that the product form may fail to satisfy the consumers’ expectations or may induce an unanticipated consumer response. In an attempt to address this problem, many systematic product form design methodologies have been proposed in recent years to minimize the requirement for subjective judgments on the part of the designer and to objectively relate the form features of a product to the emotional response induced by the product in the consumer. Kansei Engineering (KE) has emerged as one of the most powerful techniques for taking explicit account of the correlation between the physical form of a product and its projected image. Traditional KE approaches are commonly based on the concept of “items” and “categories”, defined in pictorial terms and used to generate high-level qualitative descriptions of the overall product form. However, since such approaches can not reliably predict the effects on the consumers’ emotional response of introducing subtle changes in the product form, the input of an experienced designer is still required to subjectively conjecture the consumers’ likely perception of the product’s projected image. Consequently, a requirement exists for sophisticated mechanisms capable of describing the form of a product in an explicit manner such that the correlation between the individual product form features and the consumers’ perception of the product image can be more reliably modeled. However, such systems typically require the use of a large number of variables to accurately describe the product form, and thus the problem of identifying the particular sub-set of design variables which govern the consumer’s psychological response to the product is inevitably complex. ccordingly, this study commences by developing a parametric Kansei design procedure in accordance with the numerical definition-based systematic approach (NDSA) for generating an explicit numerical definition of a product’s geometrical form. A series of evaluation trials are then performed to establish the correlation between the product form features and the consumers’ perceptions of the product image. The results of the evaluation trials are used to construct three different types of mathematical model, namely a multiple regression analysis (MRA) model, a back-propagation neural network (BPN) model, and a combined multiple regression analysis / back-propagation neural network (MRBPN) model, to predict the likely consumer response to any arbitrary product form designed in accordance with the NDSA of parametric Kansei design procedure. Evaluating the predictive performance of the three models, it is found that the MRBPN model not only yields an acceptable level of prediction accuracy, but also enables the influential design parameters to be sieved from the general design variables when constructing the predictive model such that the designer can more readily produce desirable product forms in an efficient and cost effective manner. The feasibility of the proposed NDSA of parametric Kansei design procedure is demonstrated using two illustrative product form examples, namely a 2D automobile profile and a 3D knife form, respectively. Although this study takes just two examples for illustration and verification purposes, the methodology proposed in this thesis is equally applicable to any form of consumer product.