Enabling parametric design space exploration by non-designers

Eduard Castro E Costa, Joaquim Jorge, Aaron D. Knochel, José Pinto Duarte

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)160-175
Number of pages16
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume34
Issue number2
DOIs
StatePublished - May 1 2020

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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