About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
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Presentation Title |
The Modern-day Blacksmith |
Author(s) |
Gareth Conduit |
On-Site Speaker (Planned) |
Gareth Conduit |
Abstract Scope |
We present a machine learning tool, Alchemite, that merges all possible sources of information about a material: simulations, physical laws, and experimental data. Starting from a database of CALPHAD predictions we train a machine learning model that can understand phase behavior, and use this model as an input to predict other material properties. We illustrate the approach with a case study that starts from a training set comprising just ten core results for alloy direct laser deposition, and use Alchemite to augment these with phase behavior & complementary material properties. We ask the model to design the composition and processing parameters of a nickel-base alloy for direct laser deposition, whose properties are then experimentally verified.[1]
[1] Probabilistic neural network identification of an alloy for direct laser deposition
B.D. Conduit, T. Illston, S. Baker, D. Vadegadde Duggappa, S. Harding, H.J. Stone & G.J. Conduit
Materials & Design 168, 107644 (2019) |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Additive Manufacturing, Computational Materials Science & Engineering |