Abstract
In this paper, various kinds of applications are presented, in which tomographic image data depicting microstructures of materials are semantically segmented by combining machine learning methods and conventional image processing steps. The main focus of this paper is the grain-wise segmentation of time-resolved CT data of an AlCu specimen which was obtained in between several Ostwald ripening steps. The poorly visible grain boundaries in 3D CT data were enhanced using convolutional neural networks (CNNs). The CNN architectures considered in this paper are a 2D U-Net, a multichannel 2D U-Net and a 3D U-Net where the latter was trained at a lower resolution due to memory limitations. For training the CNNs, information was derived from 3D X-ray diffraction (3DXRD) measurements. The grain boundary images enhanced by the CNNs were then segmented using a marker-based watershed algorithm with an additional postprocessing step for reducing oversegmentation. The segmentation results obtained by this procedure were quantitatively compared to information derived by the 3DXRD measurements. A quantitative comparison between segmentation results indicates that the 3D U-Net performs best among the considered U-Net architectures. Additionally, a scenario, in which ground truth data is only available in one time step, is considered. Therefore, a CNN was trained only with CT and 3DXRD data from the last measured time step. The trained network and the image processing steps were then applied to the entire series of CT scans. The resulting segmentations exhibited a similar quality compared to those obtained by the network which was trained with the entire series of CT scans.
Highlights
In materials science, supervised machine learning techniques are used to describe relationships between the microstructure of materials and their physical properties (Stenzel et al, 2017; Xue et al, 2017)
A visual comparison between grain boundaries extracted from the segmentation utilizing a 3D U-Net and the true grain boundaries obtained by 3D X-ray diffraction (3DXRD) microscopy indicates that the segmentation is reasonably good, with some oversegmented grains remaining, see Figures 10E,F
A more quantitative comparison becomes available by the grain matching procedure described in section 3.2, i.e., we will compute quantities to measure how much grains segmented from computed tomography (CT) deviate from matched grains observed in the ground truth data
Summary
In materials science, supervised machine learning techniques are used to describe relationships between the microstructure of materials and their physical properties (Stenzel et al, 2017; Xue et al, 2017). Speaking, these techniques provide high-parametric regression or classification models. The non-trivial task of segmentation can range from determining the material phases that are present in image data to the detection and extraction of single particles, grains or fibers. The quality of the segmentation has a significant influence on the subsequent analysis of the material’s microstructure and macroscopic physical properties
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