A data-driven approach for predicting printability in metal additive manufacturing processes

Mycroft, W., Katzman, M. orcid.org/0000-0001-7553-3520, Tammas-Williams, S. et al. (4 more authors) (2020) A data-driven approach for predicting printability in metal additive manufacturing processes. Journal of Intelligent Manufacturing. ISSN 0956-5515

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Additive manufacturing; Machine learning; Powder bed fusion; Electron beam melting; Printability analysis
Dates:
  • Accepted: 14 January 2020
  • Published (online): 7 February 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Funding Information:
FunderGrant number
Engineering and Physical Sciences Research CouncilEP/P030262/1
Depositing User: Symplectic Sheffield
Date Deposited: 17 Jan 2020 16:15
Last Modified: 20 Feb 2020 16:23
Status: Published online
Publisher: Springer Verlag
Refereed: Yes
Identification Number: https://doi.org/10.1007/s10845-020-01541-w

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