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

Additive manufacturing is a rapidly growing fabrication process due to its versatility and fast manufacturing capability. Additively manufactured (AM) 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 often 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 that can rapidly identify regions with defects in AM parts and scan the regions of interest (ROIs) with high resolution. Mechanical properties of AM parts will be evaluated and these data will be used to train the machine learning algorithms to build artificial intelligence (AI) into the system. The Phase I program has successfully demonstrated the feasibility of our novel approach, and the Phase II program is aimed at developing a TRL 5 system which can then be translated into commercial space. Approved for Public Release | 22-MDA-11102 (22 Mar 22)