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

SCRIPTA MATERIALIA

卷:146

Article

Conduit, B. D.¹  Jones, N. G.²  Stone, H. J.²  Conduit, G. J.³ 

[1] Rolls Royce Plc, POB 31, Derby DE24 8BJ, England

[2] Univ Cambridge, Dept Mat Sci & Met, 27 Charles Babbage Rd, Cambridge CB3 0FS, England

[3] Univ Cambridge, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England

关键词: Modeling;    Refractory metals;    Forging;    Mechanical properties;    Neural network;   

DOI  :  10.1016/j.scriptamat.2017.11.008

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free