Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913443319119872 |
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| author | Rachel Chen Lee, Juheon Gan, Chuang Yang, Zijiang Nabian, Mohammad Amin Zeng, Jun |
| author_facet | Rachel Chen Lee, Juheon Gan, Chuang Yang, Zijiang Nabian, Mohammad Amin Zeng, Jun |
| contents | Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we use a graph-based deep learning approach to predict the part deformation, which can speed up the deformation simulation substantially at the voxel level. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value. The tested accuracy on example complex geometry achieves 0.7um mean deviation for a 63mm testing part. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Virtual Foundry Graphnet for Metal Sintering Deformation Prediction Rachel Chen Lee, Juheon Gan, Chuang Yang, Zijiang Nabian, Mohammad Amin Zeng, Jun Machine Learning Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we use a graph-based deep learning approach to predict the part deformation, which can speed up the deformation simulation substantially at the voxel level. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value. The tested accuracy on example complex geometry achieves 0.7um mean deviation for a 63mm testing part. |
| title | Virtual Foundry Graphnet for Metal Sintering Deformation Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.11753 |