Laser powder bed fusion (LPBF) additive manufacturing (AM) presents a unique opportunity to create geometries, such as lattice structures, which are impossible to manufacture using traditional methods. Lattice structures are favored for their high strength-to-weight ratios, tunable and gradable properties, and energy absorption capacity. However, due to their feature size, (e.g., struts/walls as small as 200 µm), lattice performance is detrimentally impacted by the surface topography, defects, and heterogeneities characteristic of LPBF, which are inextricably linked to manufacturing parameters. While the performance impacts of these defects is understood to be severe, the mechanisms of their creation, manufacturing strategies for mitigation, and effects on performance are either underdeveloped or not yet fully understood. To address this knowledge gap, this study focuses on understanding the influence of manufacturing parameters on structural outcomes by modeling the process-structure (PS) relationships in microscale LPBF features. Herein, it is demonstrated that statical and machine learning models can predict geometric characteristics of lattices with up to 98% accuracy.
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