Abstract
Research in this article presents a computational analysis of effects of defects in 2.5D lattice structures fabricated by Additive Manufacturing (AM). Components resulting from AM often suffer from rough surfaces and porosity defects. This complicates their response which must be understood for their service deployment. The core of the methodology used in this research is a workflow for generating various defects such as surface roughness and porosity analogous to those that naturally result from AM-fabricated lattices. Surface roughness is introduced by either discretizing a sinusoidal function or fitting experimental roughness data by Fourier Transforms. Porosity defects are implemented by drawing ellipses with assigned center position, radius and aspect ratio. A plane stress Finite Element Method (FEM) model is used under a uniform displacement boundary condition. Stress-strain and stiffness of the lattices are characterized as a function of the implanted defect. This methodology enables characterization of the effect of: (i) surface roughness, (ii) porosity defect density, (iii) porosity defect size, and (iv) algorithms with which random defects can be generated in simulated specimens. Effectiveness of this workflow also provides an efficient way to generate an adequate data pool for future machine learning and other data processing work.
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