| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Combining Multi-Physics Simulations with Machine Learning to Elucidate Spatter Mechanisms and Establish Process Map in Laser Powder Bed Fusion |
| 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 predominant metal additive manufacturing (AM) method. Nevertheless, its widespread adoption for manufacturing parts across various sectors is restricted by defects that impair the mechanical properties of the components. 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 presentation, we will detail the analysis of spatter and melt pool using datasets obtained from modeling LPBF processes with an open-source tool, OpenFOAM. Additionally, we will explore the mechanistic understanding gained from applying our dataset to classification tasks using machine learning models. Our research concludes with the creation of a detailed process map that uses machine learning to adjust processing parameters and reduce defects, thus improving AM's practicality for industrial uses. |