A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images
This provides a more efficient alternative to machine learning methods for roughness field generation in computational fluid dynamics simulations, though it is incremental as it adapts an existing turbulence method to a new domain.
The paper tackles the problem of generating synthetic roughness fields for additively manufactured surfaces from limited data, presenting a Fourier mode-based method that extrapolates from a single scan to produce fields that closely approximate the original's spectral energy and structures.
A method to infer and synthetically extrapolate roughness fields from electron microscope scans of additively manufactured surfaces using an adaptation of Rogallo's synthetic turbulence method [R. S. Rogallo, NASA Technical Memorandum 81315, 1981] based on Fourier modes is presented. The resulting synthetic roughness fields are smooth and are compatible with grid generators in computational fluid dynamics or other numerical simulations. Unlike machine learning methods, which can require over twenty scans of surface roughness for training, the Fourier mode based method can extrapolate homogeneous synthetic roughness fields using a single physical roughness scan to any desired size and range. Five types of synthetic roughness fields are generated using an electron microscope roughness image from literature. A comparison of their spectral energy and two-point correlation spectra show that the synthetic fields closely approximate the roughness structures and spectral energy of the scan.