Methodology for mapping form design elements with user preferences using Kansei engineering and VDI

Abstract

In product development, decisions about the appearance of the product are risky and difficult to make. Engineers and designers are aware that adding new design features or form design elements can degrade the visual appearance. Therefore, it is important to understand how future users perceive different design configurations. In this paper, an adapted Kansei Engineering (KE) methodology focusing on the extraction of affective attributes in product design is presented. The methodology is demonstrated using a case study in which we investigated the influence of e-bike form design elements on user perception. The study was conducted using 15 pairwise adjectives to describe feelings and a set of collected e-bike image samples with different product designs, converted to silhouettes. In addition to methodological refinement, a space of properties, specifically form design elements were categorised based on VDI 2223 guidelines. Semantic space was defined using predefined affective attributes and later reduced using factor analysis, while e-bike image similarity was exploited using the Agglomerative Hierarchical Clustering (AHC) method. Influential form design elements were extracted using the decision tree method for classification based on a C4.5 algorithm. Using this methodology, we succeeded in discovering key form design elements that determine user perception.

KEYWORDS:

• Product design
• Kansei engineering
• product development
• data mining
• VDI 2223

Acknowledgements

We thank the participants in this study for their contribution. We also thank the editor of the Journal of Engineering Design and the anonymous reviewers for their helpful and insightful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://rdocumentation.org/packages/psych/versions/2.1.3