| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Synergizing Machine Learning and Multiphysics Simulation for Spatter Process Map Generation in LPBF Processes |
| Author(s) |
Olabode Ajenifujah, Odinakachukwu Ogoke, Florian Wirth, Jack Beuth, Amir Barati-Farimani |
| On-Site Speaker (Planned) |
Olabode Ajenifujah |
| Abstract Scope |
Laser powder bed fusion (LPBF) is the most common metal additive manufacturing (AM) process. However, its full utilization for part production across different industries is inhibited by defects, which limit parts' mechanical properties. Spatters are offshoots that occur due to the complex melt pool dynamics during laser-material interactions in LPBF. Spatter generation is known to promote the formation of defects such as porosity and roughness. In this talk, we will present the characterization of the spatter and the meltpool, using datasets that was procured from the result of modelling LPBF processes via an open-source software known as OpenFOAM. Furthermore, we will discuss the mechanistic insights we derived from evaluating our dataset for classification tasks via machine learning models. Our study culminates in the development of a comprehensive process map, leveraging machine learning to optimize process parameters and mitigate defects, thereby enhancing AM's viability in industrial applications. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |