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Application of computational tools in alloy design

Published online by Cambridge University Press:  09 April 2019

Akane Suzuki ,
Chen Shen  and
Natarajan Chennimalai Kumar
Akane Suzuki
Affiliation:
GE Research, USA; [email protected]
Chen Shen
Affiliation:
GE Research, USA; [email protected]
Natarajan Chennimalai Kumar
Affiliation:
GE Research, USA; [email protected]
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Abstract

Alloy design is critical to achieving the target performance of industrial components and products. In designing new alloys, there are multiple property requirements, including mechanical, environmental, and physical properties, as well as manufacturability and processability. Computational models and tools to predict properties from alloy compositions and to optimize compositions for multiple objectives are essential in enabling efficient, robust alloy design. Data-driven property models by machine learning (ML) are particularly useful in predicting physical properties with relatively simple dependence on composition, and in predicting complex properties that are too difficult for a physics-based model to achieve with desirable accuracy. In this article, we describe examples of ML applications to model coefficient of thermal expansion, creep and fatigue resistance in designing Ni-based superalloys, and optimization methodologies. We also discuss physics-based microstructure models that have been developed for optimizing heat-treatment conditions to achieve desired microstructures.

Keywords

alloychemical compositionmicrostructuresimulationstrength
Type
Computational Design And Development Of Alloys
Information
MRS Bulletin , Volume 44 , Issue 4: Computational design and development of alloys , April 2019 , pp. 247 - 251
Copyright
Copyright © Materials Research Society 2019 

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