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Title: Data-driven design strategy in fused filament fabrication : status and opportunities
Authors: Zhang, Yongjie
Moon, Seung Ki
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Zhang, 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.
Journal: Journal of Computational Design and Engineering 
Abstract: 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.
ISSN: 2288-5048
DOI: 10.1093/jcde/qwaa094
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
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles
SC3DP Journal Articles

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