Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Abstract

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing.

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

“…Scientific machine learning (ML), including deep learning methods, has emerged in recent years as a powerful family of computational tools that complement and extend the capabilities of traditional computational materials science toolkit [11,12]. In the domain of microstructure characterization, convolutional neural networks (CNN) and related methods have been successfully applied to various image analysis tasks such as feature (grain size, aspect ratio, spacing, etc) extraction and quantification, microstructure classification, image denoising and super-resolution, defect detection, semantic image segmentation, and even 2D and 3D microstructure generation [13][14][15][16][17][18][19][20]. As such, CNN-based deep learning methods are becoming part of the standard toolkit for static image processing for microscopy experiments.…”