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arXiv · 2404.11753

Virtual Foundry Graphnet for Metal Sintering Deformation Prediction

Rachel·Chen·Juheon Lee·Chuang Gan·Zijiang Yang·Mohammad Amin Nabian·Jun Zeng
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Abstract

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.

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Rachel, Chen, Juheon Lee, Chuang Gan, Zijiang Yang, Mohammad Amin Nabian, Jun Zeng. 2024-04-17. Virtual Foundry Graphnet for Metal Sintering Deformation Prediction. https://doi.org/10.18494/sam4883

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