Data-frugal Deep Learning Optimizes Microstructure Imaging - News - Carnegie Mellon University

Deep learning is often recognized as the magic behind self-driving cars and facial recognition, but what about its ability to safeguard the quality of the materials that make up these advanced devices? Carnegie Mellon University Professor of Materials Science and Engineering Elizabeth Holm and materials science and engineering doctoral student Bo Lei have adopted computer vision methods for microstructural images that not only require a fraction of the data deep learning typically relies on, but can save materials researchers an abundance of time and money. Quality control in materials processing requires the analysis and classification of complex material microstructures. For instance, the properties of some high-strength steels depend on the amount of lath-type bainite in the material. However, the process of identifying bainite in microstructural images is time-consuming and expensive as researchers must first use two types of microscopy to take a closer look and then rely on their own expertise to identify bainitic regions.