4.3 Article

3D Minimum Channel Width Distribution in a Ni-Base Superalloy

Journal

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s40192-022-00290-3

Keywords

Automated image analysis; Quantification of microstructure; Feature extraction; ?/?' -microstructure; Ni-Base superalloys; Channel width

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This paper presents a novel approach to evaluate the matrix channel width distribution in a matrix/gamma' microstructure, which relies on precise determination of the matrix/precipitate interfaces and requires no additional user input. The method is demonstrated on the example of the commercial alloy CSMX-4 and shows good performance in handling both 2D micrographs and 3D phase-field simulation data. The obtained channel width distributions compare well between the 2D and 3D data.
The strength of a Ni-base superalloy depends strongly on its microstructure consisting of cuboidal gamma' precipitates surrounded by narrow channels of y matrix. According to the theory of Orowan, a moving dislocation has to crimp through the minimal inter-precipitate spacing to admit the plastic deformation. We present a novel approach to evaluate the matrix channel width distribution of a matrix/gamma' microstructure in binary representation. Our method relies on precise determination of the matrix/ precipitate interfaces and requires no additional user input. For each matrix channel between two neighboring precipitates, we identify the minimal interface to interface distance vector with its length being the channel width. The performance of this method is demonstrated on the example of the commercial alloy CSMX-4. We show that, in contrast to conventional line sectioning approaches, the approach consistently handles experimental 2D micrographs and 3D phase-field simulation data. The identified distance vectors correlate to the underlying crystal symmetry independent of the image orientation. The obtained channel width distributions compare well between the 2D and 3D data. This is in terms of similar median and sigma of a log-normal distribution. The presented method overcomes limitations of the conventional line slicing approaches and provides a versatile tool for automated microstructure characterization.

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