In order to harness the immense amount of data obtained from sensors within the additive manufacturing (AM) machines, build planning, and post-build imaging analysis throughout the entire AM pipeline, this project aims to compile and consolidate existing multi-modal data sets and establish protocols to address future data generation.
By addressing data storage capacity, particularly for large CT data sets; file naming conventions; and data retrieval standards, Carnegie Mellon researchers will have better access to data needed for applying machine learning tools to advancing AM technology.