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A method to infer and synthetically extrapolate roughness fields fromelectron microscope scans of additively manufactured surfaces using anadaptation of Rogallo's synthetic turbulence method [R. S. Rogallo, NASATechnical Memorandum 81315, 1981] based on Fourier modes is presented. Theresulting synthetic roughness fields are smooth and are compatible with gridgenerators in computational fluid dynamics or other numerical simulations.Unlike machine learning methods, which can require over twenty scans of surfaceroughness for training, the Fourier mode based method can extrapolatehomogeneous synthetic roughness fields using a single physical roughness scanto any desired size and range. Five types of synthetic roughness fields aregenerated using an electron microscope roughness image from literature. Acomparison of their spectral energy and two-point correlation spectra show thatthe synthetic fields closely approximate the roughness structures and spectralenergy of the scan.

A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images