Probabilistic design of a molybdenum-base alloy using a neural network
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Conduit, B., Jones, N., Stone, H., & Conduit, G. (2018). Probabilistic design of a molybdenum-base alloy using a neural network. Scripta Materialia, 146 82-86. https://doi.org/10.1016/j.scriptamat.2017.11.008
An artificial intelligence tool is exploited to discover and characterize a new molybdenum-base alloy that is the most likely to simultaneously satisfy targets of cost, phase stability, precipitate content, yield stress, and hardness. Experimental testing demonstrates that the proposed alloy fulfills the computational predictions, and furthermore the physical properties exceed those of other commercially available Mo-base alloys for forging-die applications.
modeling, refractory metals, forging, mechanical properties, neural network
The authors acknowledge the financial support of Rolls-Royce plc, EPSRC under EP/H022309/1 and EP/H500375/1, the Royal Society, and Gonville & Caius College.
UNIVERSITY OF BIRMINGHAM (FB EPSRC) (EP/H022309/1)
External DOI: https://doi.org/10.1016/j.scriptamat.2017.11.008
This record's URL: https://www.repository.cam.ac.uk/handle/1810/275807
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
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