Abstract Scope |
Computer vision and machine learning techniques can be applied to microstructural images and data to make predictions and connections between the microstructure and material responses. These methods can be applied to large amounts of data from a diverse set of images, when there is enough training data. Convolution neural networks (CNNs) are a crucial tool in analyzing fatigue fracture surface images and connecting their microstructure and fatigue characteristics to loading values, crack length, and crack growth rate. This project collects data from Ti-6Al-4V fracture surfaces in the form of BSE and SEM images, and height data from Scanning White Light Interferometry, and combines them to train a CNN and identify high stress points, crack initiation sites, and predict values such as stress intensity factor and crack growth rate. The images used to develop this model, creation of the CNNs, identified fatigue properties, and fracture characteristics will be presented. |