| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Presentation Title |
Applying Generative Deep Learning Models for Cost-Effective Monitoring and Simulation of LPBF Processes |
| Author(s) |
Odinakachukwu Francis Ogoke, Quanliang Liu, Sumesh Kalambettu Suresh, Jesse Adamczyk, Dan Bolintineanu, Olabode Ajenifujah, Alexander Myers, Guadalupe Quirarte, Anthony Garland, Jack Beuth, Jonathan Malen, Michael J. Heiden, Amir Barati Farimani |
| On-Site Speaker (Planned) |
Odinakachukwu Francis Ogoke |
| Abstract Scope |
The stochastic formation of defects during Laser Powder Bed Fusion (LPBF) introduces variation in the mechanical and fatigue properties of printed parts. Generative deep learning models can be used to achieve insights into the distribution of defects produced during these processes, reducing both the cost of simulation-based forecasting and the cost of experimental monitoring of defect formation. We demonstrate this through two case studies. Initially, we develop deep learning models to stochastically upscale low-fidelity multiphysics simulations of the melting process to their high-fidelity counterparts, identifying keyhole-forming behavior while bypassing the runtime required for high-fidelity simulations. Subsequently, we apply generative models to link low-cost, low-resolution, layer-wise optical images of the build plate to detailed high-resolution images of the build plate, enabling cost-efficient layer-wise process monitoring. We evaluate the performance of these models by analyzing the statistical properties of the generated samples in addition to the preservation of key LPBF-based metrics. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |