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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.01345 |
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| _version_ | 1866929196476923904 |
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| author | Keesom, T. B. Popov, P. P. Dhyani, P. Jacobs, G. B. |
| author_facet | Keesom, T. B. Popov, P. P. Dhyani, P. Jacobs, G. B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01345 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images Keesom, T. B. Popov, P. P. Dhyani, P. Jacobs, G. B. Computer Vision and Pattern Recognition 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. |
| title | A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.01345 |