Virtual Foundry Graphnet for Metal Sintering Deformation Prediction
Rachel Chen Juheon Lee Chuang Gan Zijiang Yang Mohammad Amin Nabian Jun Zeng
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.