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Completed TRL 2 (started at 2, targeting 3)
The metal additive manufacturing (MAM) process is known to lead to defects and heterogeneities that affect the load-bearing capabilities and fatigue life of the end-product. Specifically, these defects cause higher accumulation of permanent damage, like plastic strain, leading to higher likelihoods of fatigue crack initiation during cyclic loading and ultimately premature failure. Keyhole pore geometry, pore distribution within the material, and surface roughness are defects that are particularly influential. Furthermore, the heterogeneity of material microstructure that results from the MAM process requires the consideration of correspondingly heterogeneous strain fields and highlights the importance of pore size. A predictive model of plastic strain accumulation (PSA) that is linked to pore size, aspect ratio, orientation relative to the loading direction, distance to the free surface, and surface roughness is proposed. This predictive model will be generated by collecting data from finite element (FE) simulations of idealized keyhole pore geometries and X-ray computed tomography (XCT) reconstructed surfaces, then curve-fitting using symbolic regression. Furthermore, the material microstructure of MAM components will also be considered through crystal plasticity FE simulations. With this unified framework of PSA, it will be possible to gauge the fatigue performance and propensity for fatigue crack initiation in MAM structures. The proposed research goes further by conducting uncertainty quantification analysis to output the probability distribution of PSA, thereby capturing the inherent uncertainty in MAM processes when predicting fatigue crack initiation in different areas of an MAM component.
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