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Thermal Modeling in Metal Additive Manufacturing Using Graph Theory
The long-term goal of this research is to ensure the flaw-free production of metal parts made using additive manufacturing (AM, 3D printing). As a step towards this long-term goal, the goal of this dissertation is to understand and predict the spatiotemporal temperature distribution ̶ thermal history ̶ of metal parts as they are being printed using a specific type of AM process called laser powder bed fusion (LPBF). This is an important research area, because, despite the considerable cost and time savings achieved, precision-driven industries, such as aerospace and biomedical are reticent in using LPBF to make safety-critical parts due to the tendency of the process to create flaws. The thermal history is the root cause of flaw formation in LPBF parts. Indeed, one in three LPBF parts tend to fail due to uneven heating and cooling during printing. Hence, fast and accurate prediction of the thermal history is the key to accelerate the time-to-market of LPBF parts, as well as ensure their safe function in industries important to the national prosperity. In pursuit of this goal, the objective of this dissertation is to develop, verify, validate and apply a novel computational thermal modeling approach based on the theory of heat diffusion on graphs (graph theory) to predict the thermal history in LPBF parts as they are being printed. This work tests the hypothesis that the graph theory approach predicts the thermal history of LPBF parts three to five times faster than finite element modeling for a comparable level of accuracy.
Yavari, Reza, "Thermal Modeling in Metal Additive Manufacturing Using Graph Theory" (2021). ETD collection for University of Nebraska - Lincoln. AAI28712926.