In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach

Atwya, M. orcid.org/0000-0003-2699-1415 and Panoutsos, G. orcid.org/0000-0002-7395-8418 (2023) In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach. Journal of Intelligent Manufacturing, 35 (6). pp. 2719-2742. ISSN 0956-5515

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Item Type: Article
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© 2023 The Author(s). 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://creativecomm ons.org/licenses/by/4.0/.

Keywords: Prior-guided neural network; Machine learning; Structure optimization; Metal laser powder bed fusion; Porosity
Dates:
  • Published: August 2023
  • Published (online): 3 July 2023
  • Accepted: 17 June 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
Funder
Grant number
Engineering and Physical Sciences Research Council
EP/P006566/1
Depositing User: Symplectic Sheffield
Date Deposited: 29 Nov 2023 15:24
Last Modified: 08 Jul 2024 12:03
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: 10.1007/s10845-023-02170-9
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