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.

采用傅里叶模式的方法从电子显微镜扫描的添加制造表面中推断和合成粗糙度场，使得合成的粗糙度场光滑并且与计算流体动力学中的网格生成器或其他数值模拟相兼容。与机器学习方法不同，傅里叶模式的方法只需要一次物理粗糙度扫描即可将均匀的合成粗糙度场外推到任意所需大小和范围。从文献中使用电子显微镜粗糙度图像生成了五种类型的合成粗糙度场，它们的频谱能量和双点相关谱的比较表明合成场与扫描的粗糙度结构和频谱能量非常接近。

从图像中合成增材制造粗糙度表面的模态生成