Ceramic components are important for commercial and strategic uses, ranging from tooling for materials processing and machining to biomedical, energy storage, combustion, and other applications involving extreme environments. Additive manufacturing approaches to produce ceramics include binder jetting and robocasting of green ceramic parts, which are then sintered at elevated temperatures to produce finished ceramic components. Progress in this area is hampered by directional and spatial variations in the packing density of the ceramic powder and multiple-scale defects within additively-manufactured green ceramics. The result is undesired variations in the properties and dimensional tolerances of the final parts. 

This project will integrate experiments, theory, simulations, and data science expertise to develop a new theory of sintering-assisted additive manufacturing to predict the structure, properties, and dimensional changes of finished ceramic components. The effort will use data-driven approaches to solve the inverse problem, so that the required additive manufacturing and sintering conditions to achieve desired high performance, high tolerance ceramic components can be specified in advance. Anticipated outcomes include the tools and knowledge to significantly reduce trial and error approaches to process advanced ceramics. A diverse team of undergraduate and graduate students will be trained in the principles of the Materials Genome Initiative, with experiential learning from three industrial partners, the Air Force Research Laboratory, and two international research centers. The research findings will be incorporated into graduate and undergraduate courses taught by the investigators and meaningful partnerships to reach out to middle and high school students will be developed.