Notley, S.V. orcid.org/0000-0002-8000-1809, Chen, Y., Thacker, N.A. et al. (2 more authors) (2023) Synchrotron imaging derived relationship between process parameters and build quality for directed energy deposition additively manufactured IN718. Additive Manufacturing Letters, 6. 100137. ISSN 2772-3690
Abstract
Laser additive manufacturing is transforming several industrial sectors, especially the directed energy deposition process. A key challenge in the widespread uptake of this emerging technology is the formation of undesirable microstructural features such as pores, cracks, and large epitaxial grains. The trial and error approach to establish the relationship between process parameters and material properties is problematic due to the transient nature of the process and the number of parameters involved. In this work, the relationship between process parameters, melt pool geometry and quality of build measures, using directed energy deposition additive manufacturing for IN718, is quantified using neural networks as generalised regressors in a statistically robust manner. The data was acquired using in-situ synchrotron x-ray imaging providing unique and accurate measurements for our analysis. An analysis of the variations across repeated measurements show heteroscedastic error characteristics that are accounted for using a principled nonlinear data transformation method. The results of the analysis show that surface roughness correlates with melt pool geometry while the track height directly correlates with process parameters indicating a potential to directly control efficiency and layer thickness while independently minimising surface roughness.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier B.V. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Laser additive manufacturing; Directed energy deposition; Neural networks; Meltpool geometry; In-situ x-ray imaging |
Dates: |
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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 SCIENCE RESEARCH COUNCIL EP/P006566/1 EUROPEAN COMMISSION - HORIZON 2020 820776 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Mar 2023 10:01 |
Last Modified: | 23 Mar 2023 10:01 |
Published Version: | http://dx.doi.org/10.1016/j.addlet.2023.100137 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.addlet.2023.100137 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197643 |