SEraMic: A semi-automatic method for the segmentation of grain boundaries

Abstract

The SEraMic method, implemented in the SEraMic plugin for Fiji or ImageJ software, was developed to calculate a segmented image of a ceramic cross section that shows the grain boundaries. This method was used to accurately and automatically determine grain boundary positions and further assess the grain size distribution of monophasic ceramics, metals, and alloys. The only required sample preparation is polishing the cross section to a mirror-like finish. The SEraMic method is based on at least six backscattered electron scanning electron microscopy images of a unique region of interest with various tilt angles ranging from -5° to +5°, which emphasises the orientation contrasts of the grains.Because the orientation contrast varies with the incident beam angle on the sample, the set of images contains information related to all the grain boundaries. The SEraMic plugin automatically calculates and builds a segmented image of the grain boundaries from the set of tilted images. The SEraMic method was compared with classical thermal etching methods, and it was applied to determine the grain boundaries in various types of materials (oxides, phosphates, carbides, and alloys). The method remains easy to use and accurate when the average grain diameter is greater than or equal to 0.25 µm.

Direct sintering of UO2+x oxides prepared under hydrothermal conditions

“…The determination of average grains size was performed using the SEraMic method reported by Podor et al [23], and based on the recording and the processing of series of SEM images in backscattered electron mode (BSE). Samples were first polished to a mirror grade, then SEM observations were conducted with a FEI Quanta 200 ESEM FEG microscope without any additional sample surface treatment such as chemical or thermal etching.…”

Multiscale Attention-Based Instance Segmentation for Measuring Crystals With Large Size Variation

“…This, in turn, leads to incorrect measurement of the number and size of crystals. Therefore, post-processing strategies have been proposed to close fragmented segmentation boundaries, e.g., rule-based morphological operations like dilation and erosion [10], [11], applying the watershed algorithm to complete partially segmented crystal boundaries [7], boundary skeletonization to automatically delete or extend discontinued boundaries [12] or GAN-based approaches to automatically close gaps in areas where the boundary is occluded by impurities [8]. However, these post-processing strategies are not capable of closing all boundary gaps and may also incorrectly connect boundary lines.…”

Effect of Laser Welding Parameters with Different Fillers on Solidification Cracking and Mechanical Properties of AA7075

“…The changes in grain size, resulting from the use of different fillers in Figure 12 shows the grain morphology of the FZ welded with various fillers compared to the base metal. The ImageJ software was employed to identify and extract images displaying the grain boundaries, as depicted in Figure 12b, and statistical analysis was utilized to calculate the average grain size using the Linear Intercept Method (ASTM E112) [41,42]. Within the autogenous welded sample, the FZ exhibits an average grain size of 70 µm.…”