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dc.contributor.authorZhang, Yongjieen_US
dc.contributor.authorMoon, Seung Kien_US
dc.identifier.citationZhang, Y. & Moon, S. K. (2021). Data-driven design strategy in fused filament fabrication : status and opportunities. Journal of Computational Design and Engineering, 8(2), 489-509.
dc.description.abstractThe advent of additive manufacturing (AM) has brought about radically new ways of designing and manufacturing of end-use parts and components, by exploiting freedom of design. Due to the unique manufacturing process of AM, both design and process parameters can strongly influence the part properties, thereby enlarging the possible design space. Thus, finding the optimal combination of embodiment design and process parameters can be challenging. A structured and systematic approach is required to effectively search the enlarged design space, to truly exploit the advantages of AM. Due to lowered costs in computing and data collection in the recent years, data-driven strategies have become a viable tool in characterization of process, and researches have starting to exploit data-driven strategies in the design domain. In this paper, a state-of-the-art data-driven design strategy for fused filament fabrication (FFF) is presented. The need for data-driven strategies is explored and discussed from design and process domain, demonstrating the value of such a strategy in designing an FFF part. A comprehensive review of the literature is performed and the research gaps and opportunities are analysed and discussed. The paper concludes with a proposed data-driven framework that addresses the identified research gaps. The proposed framework encompasses knowledge management and concurrent optimization of embodiment design and process parameters to derive optimal FFF part design. Contribution of this paper is twofold: A review of the state-of-the-art is presented, and a framework to achieve optimal FFF part design is proposed.en_US
dc.description.sponsorshipEconomic Development Board (EDB)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofJournal of Computational Design and Engineeringen_US
dc.rights© 2021 The Author(s). Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleData-driven design strategy in fused filament fabrication : status and opportunitiesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchSingapore Centre for 3D Printingen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsAdditive Manufacturingen_US
dc.subject.keywordsBayesian Inferenceen_US
dc.description.acknowledgementThis research was supported by a grant from ST Engineering Aerospace, EDB-IPP, Singapore Centre for 3D Printing (SC3DP), the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.en_US
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