Evolving a psycho-physical distance metric for generative design exploration of diverse shapes

Shahroz Khan, Erkan Gunpinar, Masaki Moriguchi, Hiromasa Suzuki

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching–learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects’ perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects’ perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct.
Original languageEnglish
Article number111101
Number of pages13
JournalJournal of Mechanical Design
Volume141
Issue number11
DOIs
Publication statusPublished - 16 Sep 2019

Keywords

  • generative design
  • S-TLBO method
  • space-filling
  • parametric design
  • computer-aided design

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