| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Online Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning |
| Author(s) |
Prahalada K. Rao, Ziyad Smoqi, Aniruddha Gaikwad, Benjamin Bevans, Md Humaun Kobir, James Craig, Alan Abul-Haj, Alonso Peralta |
| On-Site Speaker (Planned) |
Prahalada K. Rao |
| Abstract Scope |
In this work we accomplished the online prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally light machine learning approaches. Q large cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. Physically relevant signatures, such as meltpool length, temperature, and ejecta characteristics, were extracted from the meltpool images. Relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95%. |
| Proceedings Inclusion? |
Undecided |