About this Abstract |
| Meeting |
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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| Symposium
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2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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| Presentation Title |
Predicting the Solidified Microstructure in Laser Powder Bed Fusion (LPBF) of Inconel 718 using Physics-Based Modeling and In-situ Sensor Data |
| Author(s) |
Kaustubh Deshmukh, Christopher Williams, Mihir Darji, Swayam Kudale, Alex Riensche, Prahalada K. Rao |
| On-Site Speaker (Planned) |
Kaustubh Deshmukh |
| Abstract Scope |
We developed and implemented a physics and sensor-based data-driven integration to predict the solidified microstructure in Inconel 718 LPBF parts. In this work, Inconel 718 LPBF parts were fabricated under different LPBF processing conditions. An experimentally validated thermal model is used to simulate the part-scale thermal history of the parts and generate the end-of-cycle temperatures and cooling times. The in-situ monitoring sensor dataset is generated via continuous thermal and optical tomography imaging. The thermal model predictions and in-situ sensor data are fused within a k-nearest neighbors (KNN) machine learning model trained to predict the solidified microstructure obtained through metallographic characterization. The approach predicted these microstructure aspects with accuracy exceeding 95%. Thus, this work takes a critical step towards rapid, in-situ, and non-destructive qualification of LPBF part quality. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |