期刊
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
卷 8, 期 2, 页码 489-509出版社
OXFORD UNIV PRESS
DOI: 10.1093/jcde/qwaa094
关键词
additive manufacturing; Bayesian inference; data-driven design; design strategies; fused filament fabrication
资金
- National Research Foundation, Prime Minister's Office, Singapore
- ST Engineering Aerospace
- EDB-IPP
- Singapore Centre for 3D Printing (SC3DP)
The advent of additive manufacturing has opened up new possibilities in design and manufacturing, but finding the optimal combination of design and process parameters remains challenging. Data-driven strategies are becoming increasingly important in design, helping to achieve optimal part design.
The 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.
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