Rapid Identification of Effects of Defects within Metal Additive Manufacturing (RIED-AM)

Additive manufacturing (AM) systems, especially metal AM, bring revolutionary capabilities, but suffer from a lack of understanding of the defects that exist within the components. In this research, based on selective experimental study and numerical simulations, we will develop an empirical database of defects and their effects on mechanical properties using Laser Powder Bed Fusion (LPBF) technology. The database can be applied to guide selection/rejection of AM components. Further, we propose utilizing Resonant Ultrasonic Spectroscopy (RUS) technique as a quick NDI tool for screening defective vs. acceptable parts. By applying lab testing, the RUS measurement results and the permissible defects can be mapped out so that RUS can be used as a pass/fail testing tool for the AM components produced. Additionally, we will utilize numerical simulations and adaptive Design of Experiments (DOE) to minimize the number of experiments and provide uncertainty estimates over results. In phase I, 316 SS or CP Titanium will be studied, and the research methodology will be applied to other material systems in Phase II. It is envisioned that the technology will provide great convenience for the quality control process of AM parts.