Can I read Virtual Foundry Graphnet for Metal Sintering Deformation Prediction on EtoBox?

Virtual Foundry Graphnet for Metal Sintering Deformation Prediction by Rachel; Chen; Lee, Juheon; Gan, Chuang; Yang, Zijiang; Nabian, Mohammad Amin; Zeng, Jun is a scholarly article available to read on EtoBox.

What is Virtual Foundry Graphnet for Metal Sintering Deformation Prediction about?

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.

Author

Rachel; Chen; Lee, Juheon; Gan, Chuang; Yang, Zijiang; Nabian, Mohammad Amin; Zeng, Jun

Published

2024

Language

EN