Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

Journal Article · Wed Mar 09 00:00:00 EST 2022 · Journal of Materials Processing Technology

DOI:https://doi.org/10.1016/j.jmatprotec.2022.117550· OSTI ID:1977320

Smoqi, Ziyad ^[1]; Gaikwad, Aniruddha ^[2]; Bevans, Benjamin ^[2]; Kobir, Md Humaun ^[2]; Craig, James ^[3]; Abul-Haj, Alan ^[4]; Peralta, Alonso ^[5]; Rao, Prahalada ^[2]

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1. Univ. of Nebraska, Lincoln, NE (United States); OSTI
2. Univ. of Nebraska, Lincoln, NE (United States)
3. Stratonics, Lake Forest, CA (United States)
4. ARA Engineering, Sedona, AZ (United States)
5. Honeywell, Phoenix, AZ (United States)

In this work we accomplished the monitoring and 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 tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity 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. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, 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% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

• Research Organization: Univ. of Nebraska, Lincoln, NE (United States)
• Sponsoring Organization: Defense Advanced Research Projects Agency (DARPA); USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: SC0021136
• OSTI ID: 1977320
• Alternate ID(s): OSTI ID: 1854944
• Journal Information: Journal of Materials Processing Technology, Journal Name: Journal of Materials Processing Technology Journal Issue: C Vol. 304; ISSN 0924-0136
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
Engineering
Imaging pyrometer
Laser powder bed fusion
Materials Science
Meltpool monitoring
Physics-informed machine learning
Porosity prediction