Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
<|reference_start|>Virtual Foundry Graphnet for Metal Sintering Deformation Prediction: 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.<|reference_end|>
@article{rachel2024virtual,
    title={Virtual Foundry Graphnet for Metal Sintering Deformation Prediction},
    author={Rachel (Lei) Chen, Juheon Lee, Chuang Gan, Zijiang Yang, Mohammad Amin
  Nabian, Jun Zeng},
    journal={Sensors and Materials 2024},
    year={2024},
    doi={10.18494/SAM4883},
    number={Sensors and Materials, Vol. 36, No. 7 (2024) 2835--2849 2835},
    archivePrefix={arXiv},
    eprint={2404.11753},
    primaryClass={cs.LG}
}