Markanday, F., Conduit, G., Conduit, B. et al. (6 more authors) (2022) Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification. Data-Centric Engineering, 3. e30. ISSN 2632-6736
Abstract
A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © University of Cambridge, 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
Keywords: | additive laser methods; alloy design; neural networks; nickel alloys; repair methods |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/P02470X/1 Engineering and Physical Sciences Research Council EP/P025285/1 Engineering and Physical Sciences Research Council EP/S019367/1 Engineering and Physical Sciences Research Council EP/R00661X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Dec 2022 11:27 |
Last Modified: | 05 Dec 2022 11:27 |
Status: | Published |
Publisher: | Cambridge University Press (CUP) |
Refereed: | Yes |
Identification Number: | 10.1017/dce.2022.31 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194036 |