Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks
Abstract Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-12-01
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| Series: | Friction |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40544-023-0826-7 |
| _version_ | 1797219632044572672 |
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| author | Junhyeon Seo Prahalada Rao Bart Raeymaekers |
| author_facet | Junhyeon Seo Prahalada Rao Bart Raeymaekers |
| author_sort | Junhyeon Seo |
| collection | DOAJ |
| description | Abstract Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications. |
| first_indexed | 2024-04-24T12:36:44Z |
| format | Article |
| id | doaj.art-de8cb8a1799247678d0de5c81e30c277 |
| institution | Directory Open Access Journal |
| issn | 2223-7690 2223-7704 |
| language | English |
| last_indexed | 2024-04-24T12:36:44Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Friction |
| spelling | doaj.art-de8cb8a1799247678d0de5c81e30c2772024-04-07T11:30:28ZengSpringerOpenFriction2223-76902223-77042023-12-011261283129810.1007/s40544-023-0826-7Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networksJunhyeon Seo0Prahalada Rao1Bart Raeymaekers2Department of Mechanical Engineering, Virginia TechGrado Department of Industrial and Systems Engineering, Virginia TechDepartment of Mechanical Engineering, Virginia TechAbstract Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.https://doi.org/10.1007/s40544-023-0826-7additive manufacturingsurface topographysynthetic surface topographygenerative adversarial networks |
| spellingShingle | Junhyeon Seo Prahalada Rao Bart Raeymaekers Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks Friction additive manufacturing surface topography synthetic surface topography generative adversarial networks |
| title | Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks |
| title_full | Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks |
| title_fullStr | Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks |
| title_full_unstemmed | Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks |
| title_short | Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks |
| title_sort | generating synthetic as built additive manufacturing surface topography using progressive growing generative adversarial networks |
| topic | additive manufacturing surface topography synthetic surface topography generative adversarial networks |
| url | https://doi.org/10.1007/s40544-023-0826-7 |
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