High-throughput in-situ characterization and modelling of precipitation kinetics in compositionally graded alloys

TL;DR

This paper presents a high-throughput in-situ SAXS method to characterize precipitation kinetics across composition gradients in alloys, enabling faster alloy development by linking microstructure evolution to processing conditions.

Contribution

It introduces a novel approach combining synchrotron SAXS with compositional gradients to monitor microstructure evolution in alloys in real-time, demonstrating its effectiveness with Cu-Co alloys.

Findings

Successfully monitored precipitate size, volume fraction, and density along the composition gradient.

Validated the applicability of a conventional precipitation model to large, complex datasets.

Showed that microstructure evolution can be effectively characterized in combinatorial alloy samples.

Abstract

The development of new engineering alloy chemistries is a time consuming and iterative process. A necessary step is characterization of the nano/microstructure to provide a link between the processing and properties of each alloy chemistry considered. One approach to accelerate the identification of optimal chemistries is to use samples containing a gradient in composition, ie. combinatorial samples, and to investigate many different chemistries at the same time. However, for engineering alloys, the final properties depend not only on chemistry but also on the path of microstructure development which necessitates characterization of microstructure evolution for each chemistry. In this contribution we demonstrate an approach that allows for the in-situ, nanoscale characterization of the precipitate structures in alloys, as a function of aging time, in combinatorial samples containing a composition gradient. The approach uses small angle x-ray scattering (SAXS) at a synchrotron beamline. The Cu-Co system is used for the proof-of-concept and the combinatorial samples prepared contain a gradient in Co from 0% to 2%. These samples are aged at temperatures between 450{\textdegree}C and 550{\textdegree}C and the precipitate structures (precipitate size, volume fraction and number density) all along the composition gradient are simultaneously monitored as a function of time. This large dataset is used to test the applicability and robustness of a conventional class model for precipitation that considers concurrent nucleation, growth and coarsening and the ability of the model to describe such a large dataset.