Rapid Determination of Strength and Fatigue in 3D Printed Parts in Production

Additive manufacturing is rapidly a growing fabrication process due to its versatility and fast manufacturing capability. Additively manufactured prints of metal parts often contain voids and intra-granular impurities which reduce the performance of the part in terms of strength and durability. These defects are difficult to detect in the finished part. The goal of the proposed research is to develop a non-destructive method to not only detect and quantify these defects, but also to correlate the results to the expected strength and fatigue life of the part. To accomplish this we will utilize complementary non-destructive evaluation (NDE) modalities to document defects. Detailed strength analysis of scanned parts will be independently performed and the data will be correlated to the NDE observations. These information will be obtained for a large number of additively manufactured components to create a training set for machine learning, which will expedite the qualification process in a production environment. The Phase I program will demonstrate feasibility of our novel method whereas the Phase II program will logically extend Phase I research to implement a practical system. Approved for Public Release | 20-MDA-10643 (3 Dec 20)