SBIR/STTR Award attributes
Current component qualification approaches for additive manufacturing (AM) are time consuming and expensive. In this effort, TDA team will develop and implement tools to allow process optimization tied to component specifications, which will be a significant advance in defining and accelerating component qualification. Following an ICME framework, TDA plans to develop a fully automated thermal-mechanical finite element numerical simulation tool to predict AM part intrinsic properties for variable input parameters. It is proposed to use an innovative data-driven stochastic framework to characterize the effect of material and process uncertainties on the mechanical performance of additively manufactured parts. Analytical approaches will be used to predict mechanical performance and also quantify uncertainty in the selected few mechanical performance parameters using a novel surrogate modeling technique. TDA’s research partner Carnegie Mellon University will perform test specimen and subcomponent fabrication and characterization on equipment within their NextManufacturing Center and in related laboratories. Methods developed at CMU for methodically controlling porosity in AM deposits will be used to guide the selection of process variables in the specimen and subcomponent builds. Multiple process monitoring techniques will be employed during AM builds, allowing for enhanced documentation of specimen and subcomponent build conditions.
