Automated identification and characterisation of secondary and tertiary γ ′ precipitates in nickel-based superalloys

Abstract

The use of different electron loss edges in energy-filtered transmission electron microscopy (EFTEM) has allowed researchers to capture images of the morphology and size of precipitates in nickel-based superalloys. In this work, the authors discuss a computational methodology for automated detection of secondary and tertiary γ′ precipitates in EFTEM images. The optimum parameters for the automated region growing technique were identified using a combination of visual inspection and intensity information from the EFTEM images. The microstructural statistics obtained from the segmented γ′ precipitates agreed with those of the manually segmented precipitates. Then, automated segmented precipitates are used to extract microstructural information about the distributions of equivalent diameters of 656 tertiary precipitates along with the distances to the nearest secondary precipitates. The significance of this technique is its ability to automate segmentation of precipitates in a reproducible manner for acquiring microstructural statistics that relate to both processing and properties.