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
Abstract
Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/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: | 10.1007/s10845-020-01541-w |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155659 |