Automated image analysis for quantification of materials microstructure evolution

Abstract

In this work, an automated image analysis procedure for the quantification of microstructure evolution during creep is proposed for evaluating scanning electron microscopy micrographs of a single crystal Ni-based superalloy before and after creep at 950 °C and 350 MPa. scanning electron microscopy-micrographs of γ/γ′ microstructures are transformed into binary images. Image analysis, which involves pixel by pixel classification and feature extraction, is then combined with a supervised machine learning algorithm to improve the binarization and the quality of the results. The binarization of the gray scale images is not always straight forward, especially when the difference in gray levels between the γ-channels and the γ′-phase is small. To optimize feature extraction, we utilized a series of bilateral filters as well as a machine learning algorithm, known as the gradient boosting method, that was used for training and classifying the micrograph pixels. After testing the two methods, the gradient boosting method was identified as the most effective. Subsequently, a Python routine was written and implemented for the automated quantification of the γ′ area fraction and the γ channel width. Our machine learning method is documented and the results of the automatic procedure are discussed based on results which we previously reported in the literature.

3D phase-field simulations to machine-learn 3D information from 2D micrographs

“…The width and height are the bounding rectangle width and length, respectively. γ channel width: Ahmed et al [46]. proposed an automated quantification method to measure the horizontal γ channel width by means of counting the pixel lengths of vertical line profiles.…”

Enhanced photodetector performance of SnO2/NiO heterojunction via Au incorporation

“…The FESEM surface images of SnO 2 /NiO without and with Au thin film along with the respective binary images processed using ImageJ software are shown in figures 5(a)-(h). Binary images in ImageJ software are a useful tool for the segmentation of overlapping particles and the determination of shape and size in microstructures [31]. It converts the required image to 8-bit and shows the contrast between the pores and surface features [32].…”

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

“…Shown in Fig. 1c is the line slicing method, which is conducted along a certain orientation relative to the precipitate orientation [28,29] or along two perpendicular directions in form of a grid [30,31]. These techniques require knowledge about the orientation of the microstructure within the image to find a physically meaningful measure for the precipitate spacing that effectively hinders dislocation movement through a matrix channel.…”