Tailoring the Properties of Multiphase Materials Through the Use of Correlative Microscopy and Machine Learning

This research uses state-of-the-art machine learning (ML) techniques in a new and novel manner to identify and correlate the critical microstructural features in a multiphase alloy that exhibits high strength and fracture toughness. Experimental data will be used to train a convolutional neural network (CNN) in a semi-supervised environment to identify key microstructural features and correlate those features with the strength and toughness. The resulting machine learning tool can be trained for additional microstructural features, different alloys, and/or target mechanical properties.