Repository logo
 

Probabilistic design of a molybdenum-base alloy using a neural network

Accepted version
Peer-reviewed

Type

Article

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

Volume Title

146

Publisher

Elsevier
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)
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.