Precipitate hardened Ni-base superalloys are used under heavy mechanical loads at high operating temperatures. The superalloys’ mechanical stability at these harsh conditions is related to their distinct microstructure at nanometer scale. Coherent particles are deliberately precipitated in the matrix phase to achieve a defined particle size, shape and volume fraction. This microstructure leads to high mechanical strength even near the materials melting points. Its quantification is thus of interest for modeling the materials mechanical properties.

Analysis of the precipitate microstructure is typically conducted based on electron microscopy micrographs featuring contrast between the matrix and the precipitate phases. Other descriptions include three-dimensional simulation data. We present an automated routine to consistently extract the microstructure parameters of interest, the particle size, shape and the inter-particle spacing, from both common data sources.

The first step in this workflow is the segmentation of the data into the matrix and precipitate phase. For the micrographs, this is handled by a neural net trained specifically for this purpose. After that, the precipitates surrounded by matrix are identified, labeled, and isolated and their size, shape, and orientation are evaluated through calculation of their moment invariants [1]. Nearest neighbor particles are found based on their minimal surface to surface distance, which is taken as a measure for the inter-precipitate spacing, also referred to as channel width.

The presented routine results in accurate distributions of precipitate shapes, sizes and channel widths, consistent for both 2D and 3D data sources. When evaluating simulation data, special features like periodic boundary conditions are correctly considered for all parameters.

[1] F. Schleifer; M. Müller; Y.-Y. Lin; M. Holzinger; U. Glatzel; M. Fleck, Integrating Materials and Manufacturing Innovation, 2022, in revision.