Probabilistic design of a molybdenum-base alloy using a neural network
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Peer-reviewed
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Abstract
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.
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Scripta Materialia
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Journal ISSN
1359-6462
1872-8456
1872-8456
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146
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Elsevier
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Except where otherwised noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
Sponsorship
Engineering and Physical Sciences Research Council (EP/H022309/1)
Engineering and Physical Sciences Research Council (EP/H500375/1)
Engineering and Physical Sciences Research Council (EP/M005607/1)
Engineering and Physical Sciences Research Council (EP/H500375/1)
Engineering and Physical Sciences Research Council (EP/M005607/1)
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.