Enabling parametric design space exploration by non-designers

Castro e Costa, Eduardo; Jorge, Joaquim; Knochel, Aaron D.; Duarte, Jos� Pinto

Abstract

In mass customization, software configurators enable novice end-users to design customized products and services according to their needs and preferences. However, traditional configurators hardly provide an engaging experience while avoiding the burden of choice. We propose a Design Participation Model to facilitate navigating the design space, based on two modules. Modeler enables designers to create customizable designs as parametric models, and Navigator subsequently permits novice end-users to explore these designs. While most parametric designs support direct manipulation of low-level features, we propose interpolation features to give customers more flexibility. In this paper, we focus on the implementation of such interpolation features into Navigator and its user interface. To assess our approach, we designed and performed user experiments to test and compare Modeler and Navigator, thus providing insights for further developments of our approach. Our results suggest that barycentric interpolation between qualitative parameters provides a more easily understandable interface that empowers novice customers to explore the design space expeditiously.

• Journal Article Type: Article
• Acceptance Date: Jan 11, 2020
• Online Publication Date: Apr 16, 2020
• Publication Date: 2020-05
• Deposit Date: Apr 8, 2022
• Journal: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
• Print ISSN: 0890-0604
• Electronic ISSN: 1469-1760
• Publisher: Cambridge University Press (CUP)
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 2
• Pages: 160-175
• DOI: https://doi.org/10.1017/S0890060420000177
• Keywords: Artificial Intelligence; Industrial and Manufacturing Engineering
• Public URL: https://uwe-repository.worktribe.com/output/9303688