| 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 |
Microstructure Prediction in Laser Powder Bed Fusion using Heterogeneous Sensing, Process Modeling, and Machine Learning |
| Author(s) |
Prahalada K. Rao, Benjamin Bevans, Antonio Carrington, Alex Riensche, Christopher Barrett, Harold (Scott) Halliday, Adrianne Tenequer, Raghu Srinivasan |
| On-Site Speaker (Planned) |
Prahalada K. Rao |
| Abstract Scope |
In this work we predicted microstructural properties in laser powder bed fusion (LPBF) additive manufacturing of Inconel 718 alloy by combining real-time in-situ sensor data and thermal history estimates from a physics-based model within machine learning. This digital twin approach was used to predict multi-scale aspects of microstructure ranging from porosity, solidified meltpool, primary dendritic arm spacing, and microhardness. A physics-based thermal simulation model was used predict the cooling rates in the process. These cooling rate estimates along with real-time in-situ sensor data are then used as features into simple machine learning models to predict the onset of lack-of-fusion porosity, meltpool depth, grain size, and microhardness with a prediction score in excess of 90% (R-square). These models were validated across multiple part geometries and processing conditions. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |