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Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification

Published version
Peer-reviewed

Type

Article

Change log

Authors

Markanday, F 
Conduit, G 
Conduit, B 
Pürstl, J 

Abstract

jats:titleAbstract</jats:title> jats:pA neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.</jats:p>

Description

Keywords

additive laser methods, alloy design, neural networks, nickel alloys, repair methods

Journal Title

Data-Centric Engineering

Conference Name

Journal ISSN

2632-6736
2632-6736

Volume Title

3

Publisher

Cambridge University Press (CUP)
Sponsorship
Engineering and Physical Sciences Research Council (EP/S019367/1)
Engineering and Physical Sciences Research Council (EP/R00661X/1)
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