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Probabilistic neural network identification of an alloy for direct laser deposition

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Conduit, BD 
Illston, T 
Baker, S 
Duggappa, DV 
Harding, S 

Abstract

A neural network tool was used to discover a new nickel-base alloy for direct laser deposition most likely to satisfy targets of processability, cost, density, phase stability, creep resistance, oxidation, fatigue life, and resistance to thermal stresses. The neural network tool can learn property-property relationships, which allows it to use a large database of thermal resistance measurements to guide the extrapolation of just ten data entries of alloy processability. The tool was used to propose a new alloy, and experimental testing confirms that the physical properties of the proposed alloy are better tailored to the target application than other available commercial alloys.

Description

Keywords

Nickel, Direct laser deposition, Alloy, Neural network

Journal Title

Materials and Design

Conference Name

Journal ISSN

0264-1275
1873-4197

Volume Title

168

Publisher

Elsevier BV
Sponsorship
The Royal Society (uf130122)
Royal Society (RGF/EA/180034)
Engineering and Physical Sciences Research Council (EP/H500375/1)
Engineering and Physical Sciences Research Council (EP/M005607/1)
Royal Society (IMF130944)
Technology Strategy Board (113072)
Engineering and Physical Sciences Research Council (EP/H022309/1)