Probabilistic design of a molybdenum-base alloy using a neural network
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Conduit, BD
Jones, NG
Stone, HJ
Conduit, GJ
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.
Description
Keywords
modeling, refractory metals, forging, mechanical properties, neural network
Journal Title
Scripta Materialia
Conference Name
Journal ISSN
1359-6462
1872-8456
1872-8456
Volume Title
146
Publisher
Elsevier
Publisher DOI
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.