| Autor: |
Freddie Markanday, Gareth Conduit, Bryce Conduit, Julia Pürstl, Katerina Christofidou, Lova Chechik, Gavin Baxter, Christopher Heason, Howard Stone |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Data-Centric Engineering, Vol 3 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2632-6736 |
| DOI: |
10.1017/dce.2022.31 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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