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, 31 (7). pp. 1769-1781. ISSN 0956-5515



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Keywords: Additive manufacturing; Machine learning; Powder bed fusion; Electron beam melting; Printability analysis
  • Accepted: 14 January 2020
  • Published (online): 7 February 2020
  • Published: October 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: 21 Oct 2021 09:24
Status: Published
Publisher: Springer Verlag
Refereed: Yes
Identification Number: https://doi.org/10.1007/s10845-020-01541-w