Hung-Yuan Chen

Optimizing product form attractiveness using Taguchi method and TOPSIS algorithm: A case study involving a passenger car

Form attractiveness is an important catalyst driving consumer preference for a particular product. However, a designer faces two major difficulties when attempting to enhance the attractiveness of a product form. First, consumer perception regarding form attractiveness involves a number of different intricate psychological and circumstantial evaluation bases. Second, variability exists between different consumer’s perceptions of the same product form. This study treats the task of satisfying consumer needs/desires for product form as a multi-objective design activity and develops a product form optimization method, which combines the Taguchi method with the TOPSIS algorithm. A case study involving the design of a passenger car form targeted at a particular consumer group is presented to demonstrate the operational procedure involved in the proposed method and to verify its performance. The results of the verification experiments confirm that the optimized design has a higher attractiveness in terms of each evaluation basis and results in a more consistent consumer evaluation than the non-optimized designs. The method proposed in this study is straightforward to implement and provide an effective means of reducing the time and costs associated with product form design.

A neural network-based computer aided design tool for automotive form design

Although the functionality and performance are both important aspects in vehicle design, an automotive form is a crucial factor in determining a consumers image perception and purchase decision. Hence, an effective tool for designing successful automotive form was suggested. Based on Kansei Engineering principles, the relationship between the profile characteristics and the consumers image perception is established using a Back-Propagation Neural (BPN) network. A Computer Aided Design (CAD) tool, which uses the trained BPN to predict the consumer perception of an automotive profile expressed in the form of a numerical definition, is constructed using Visual Basic software. The performance of the CAD prototype tool is verified by comparing its predictions to the actual consumer perception evaluations. A good similarity is identified between the two sets of results. Therefore, the developed tool provides designers with powerful means of creating automotive designs from a consumers image perception perspective.

Comparison of Two Quantitative Analysis Techniques to Predict the Evaluation of Product Form Design

Consumer satisfaction with a product’s form plays an essential role in determining the likelihood of its commercial success. A consumer perception-centered design approach is proposed in this study to aid product designers with incorporating consumers’ perceptions of product forms in the design process. The consumer perception-centered design approach uses the linear modeling technique (multiple linear regression) and the nonlinear modeling technique (neural network) to determine the satisfying product form design for matching a given product image. A series of experimental evaluations are conducted to collect evaluation results for examining the relationship between the automobile profile features and the consumers’ perceptions of the automobile image. The result of predictive performance comparison shows that both the nonlinear neural network modeling technique and the multiple linear regression technique are comparably good for predicting the consumers’ likely response to a particular automobile profile since the predictive performance difference between the two modeling techniques is very slight in this study. Although this study has chosen a 2D automobile profile for illustration purposes, the concept of the proposed approach is expansively applicable to 3D automotive form design or other consumer product forms.

A numerical definition-based systematic design approach for coupling consumers ' image perception with product form

Purpose – This paper aims to propose a numerical definition-based systematic design approach (NDSDA) to generate an explicit numerical definition of the product form profile and to establish the correlation between the product form features and the corresponding consumers’ image perceptions. Design/methodology/approach – To illustrate the feasibility of the proposed method, this study considers the design of a two-dimensional automobile profile for illustration purposes and commences by developing a detailed numerical definition of an automobile profile using Bezier curves. A series of automobile image evaluations are conducted to examine the relationship between the characteristics of an automobile profile and its associated consumer image perception. Finally, the evaluation results are analyzed statistically, and the statistical results are used to construct mathematical models formalizing the correlations between the automobile profile design variables and the consumers’ perceptions of the product imag...

Product form feature selection methodology based on numerical definition–based design

In product design, the product form has a significant effect on the affective response it induces in potential consumers and is also of crucial importance if the product is to achieve commercial success. Intuitively, it seems reasonable to speculate that a consumer’s affective response to a product is dominated by certain critical features of the product form. To extract the product’s specific form features critical to determining consumers’ affective responses, this study proposes a product form feature selection methodology based on a numerical definition–based approach and the consumers’ affective responses. In the proposed methodology, numerical definition–based approach is used to generate an explicit numerical definition of the product form design, and the corresponding consumers’ affective responses (described using single adjectives) are determined by means of a semantic differential experiment. Two consumers’ affective response prediction models are constructed using support vector regression and multiple linear regression techniques, respectively. Finally, two feature selection methods, namely, support vector regression with support vector machine–recursive feature elimination and multiple linear regression with the stepwise procedure, are used to identify the critical form features. The validity of the two feature selection methods is demonstrated using a knife design for illustration purposes. The results show that the proposed methodology provides product designers with a powerful tool for systematically extracting the critical form features and evaluating their respective effects on the affective responses.