journal article·2016-08Open access

Application of a multi-component mean field model to the coarsening behaviour of a nickel-based superalloy

M.J. Anderson, A. Rowe, J. Wells, H.C. Basoalto

Acta Materialia· Volume 114

Abstract

A multi-component mean field model has been applied to predict the particle evolution of the γ′ particles in the nickel based superalloy IN738LC, capturing the transition from an initial multimodal particle distribution towards a unimodal distribution. Experiments have been performed to measure the coarsening behaviour during isothermal heat treatments using quantitative analysis of micrographs. The three dimensional size of the γ′ particles has been approximated for use in simulation. A coupled thermodynamic/mean field modelling framework is presented and applied to describe the particle size evolution. A robust numerical implementation of the model is detailed that makes use of surrogate models to capture the thermodynamics. Different descriptions of the particle growth rate of non-dilute particle systems have been explored. A numerical investigation of the influence of scatter in chemical composition upon the particle size distribution evolution has been carried out. It is shown how the tolerance in chemical composition of a given alloy can impact particle coarsening behaviour. Such predictive capability is of interest in understanding variation in component performance and the refinement of chemical composition tolerances. It has been found that the inclusion of misfit strain within the current model formulation does not have a significant affect upon predicted long term particle coarsening behaviour. Model predictions show good agreement with experimental data. In particular, the model predicts a reduced growth rate of the mean particle size during the transition from bimodal to unimodal distributions.
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Research context

OpenAlex topics: High Temperature Alloys and Creep · Aluminum Alloy Microstructure Properties · High-Temperature Coating Behaviors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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