| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Powder Bed Fusion Surrogate Models via Convolutional Neural Networks |
| Author(s) |
David Rosen, John Kgee Ong, U-Xuan Tan, Qing Yang Tan, Umesh Kizhakkinan, Huy Quang Do, Clive Ford |
| On-Site Speaker (Planned) |
David Rosen |
| Abstract Scope |
To support the design of structural metal parts for demanding applications, accurate predictions of part properties are needed, which high fidelity simulations can provide. However, metal powder bed fusion (PBF) simulations are far too computationally demanding, due to their very complex physical phenomena, for use in design optimization that can require dozens or hundreds of iterations. Rather, we are developing surrogate models of PBF process simulation results based on 3D convolutional neural network (CNN) technology. These CNN surrogate models compute part properties at high resolution in much less than one second. In this presentation, we summarize PBF process simulations and detail the CNN surrogate models developed for residual stress, deformation, and mechanical property distribution predictions of part designs. Examples of metal part fabrication results are compared with simulation and surrogate model predictions. Application of the surrogate models in part design optimization is illustrated. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |