Virtual Foundry Graphnet for Metal Sintering Deformation Prediction

• URL: http://arxiv.org/abs/2404.11753v1
• Date: Wed, 17 Apr 2024 21:11:12 GMT
• Title: Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
• Authors: Rachel, Chen, Juheon Lee, Chuang Gan, Zijiang Yang, Mohammad Amin Nabian, Jun Zeng,
• Abstract summary: We use a graph-based deep learning approach to predict the part deformation. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value.
• Score: 87.44136293798721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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