Image-driven deep-learning-enabled automatic microstructural recognition

Abstract

The deep learning (DL) method consisting of a convolutional neural network (CNN) was employed to automate the task of microstructural recognition and classification to identify dendritic characteristics in metallic microstructures. Dendrites are an important feature that decide the mechanical properties of an alloy; further, the dendritic arm spacing is critical in ascertaining the values of strength and ductility. The current work was divided into two tasks – namely, classification of microstructures into dendritic and non-dendritic microstructures (task 1) and further classification of the dendritic microstructures into longitudinal and transverse cross-sectional views (task 2). The data set comprised micrographs from experimental and online sources covering a broad range of alloy compositions, micrograph magnifications and orientations. The tasks were achieved by employing a four-layered CNN to yield an accuracy of 97.17 ± 0.64% for task 1 and 87.86 ± 1.07% for task 2 independently. The employment of the DL model for classification of microstructures circumvents the feature extraction step while ensuring high accuracy. This work reduces dependency on skilled and experienced researchers and expedites the material development cycle.