Journal
SCRIPTA MATERIALIA
Volume 146, Issue -, Pages 82-86Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.scriptamat.2017.11.008
Keywords
Modeling; Refractory metals; Forging; Mechanical properties; Neural network
Categories
Funding
- Rolls-Royce plc, EPSRC [EP/F1022309/1, EPP-1500375/1]
- Royal Society
- Gonville Caius College
- Engineering and Physical Sciences Research Council [EP/M005607/1] Funding Source: researchfish
- EPSRC [EP/M005607/1] Funding Source: UKRI
Ask authors/readers for more resources
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. (C) 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
