Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
This work addresses deformation prediction in metal sintering for 3D printing applications, offering a significant speed-up but is incremental as it applies an existing graph-based method to a specific domain.
The paper tackles the problem of predicting large deformations (25-50%) in metal sintering processes for 3D printing by developing a graph-based deep learning approach, achieving a mean deviation of 0.7μm for a 63mm test part and reducing simulation time to seconds.
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.