Deep Generative Design: Integration of Topology Optimization and Generative Models

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dc.contributor.authorOh, Sangeunko
dc.contributor.authorJung, Yongsuko
dc.contributor.authorKim, Seongsinko
dc.contributor.authorLee, Ikjinko
dc.contributor.authorKang, Namwooko
dc.date.accessioned2019-11-01T08:20:05Z-
dc.date.available2019-11-01T08:20:05Z-
dc.date.created2019-10-22-
dc.date.created2019-10-22-
dc.date.created2019-10-22-
dc.date.created2019-10-22-
dc.date.created2019-10-22-
dc.date.created2019-10-22-
dc.date.issued2019-11-
dc.identifier.citationJOURNAL OF MECHANICAL DESIGN, v.141, no.11-
dc.identifier.issn1050-0472-
dc.identifier.urihttp://hdl.handle.net/10203/268130-
dc.description.abstractDeep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based deep generative design framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.-
dc.languageEnglish-
dc.publisherASME-
dc.titleDeep Generative Design: Integration of Topology Optimization and Generative Models-
dc.typeArticle-
dc.identifier.wosid000506891900008-
dc.identifier.scopusid2-s2.0-85072516810-
dc.type.rimsART-
dc.citation.volume141-
dc.citation.issue11-
dc.citation.publicationnameJOURNAL OF MECHANICAL DESIGN-
dc.identifier.doi10.1115/1.4044229-
dc.contributor.localauthorLee, Ikjin-
dc.contributor.nonIdAuthorOh, Sangeun-
dc.contributor.nonIdAuthorKim, Seongsin-
dc.contributor.nonIdAuthorKang, Namwoo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle; Proceedings Paper-
dc.subject.keywordAuthorgenerative design-
dc.subject.keywordAuthordesign exploration-
dc.subject.keywordAuthortopology optimization-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthorgenerative models-
dc.subject.keywordAuthorgenerative adversarial networks-
dc.subject.keywordAuthordesign automation-
dc.subject.keywordAuthordesign methodology-
dc.subject.keywordAuthordesign optimization-
dc.subject.keywordAuthorexpert systems-
dc.subject.keywordAuthorproduct design-
dc.subject.keywordPlusFILTERS-
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