Machine learning-powered method detects LPBF pore formation in real time

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Tao Sun (right) and Zhongshu Ren have developed an approach to detect the exact moment a keyhole pore forms during the LPBF process. (Image: UVA Engineering)

Researchers have devised a method for detecting the formation of keyhole pores in laser powder bed fusion (LPBF) in real time.

Described in Science, the new method could help expand the use of additive manufacturing (AM) in aerospace, automotive, energy production, and the range of other industries now making use of the technology.

Keyhole pores are one of the major defects in LPBF. Their formation and size are a function of laser power and scanning velocity, as well as the AM materials’ capacity to absorb laser energy. 

If the keyhole walls are stable, it enhances the surrounding material’s laser absorption and improves laser manufacturing efficiency.

If, however, the walls are wobbly or collapse, the material solidifies around the keyhole, trapping the air pocket inside the newly formed layer of material. This makes the material more brittle and more likely to crack under environmental stress.

Now however, a research team led by Tao Sun, associate professor of materials science and engineering at the University of Virginia, has developed an approach to detect the exact moment a keyhole pore forms during the printing process.

“By integrating operando synchrotron x-ray imaging, near-infrared imaging, and machine learning, our approach can capture the unique thermal signature associated with keyhole pore generation with sub-millisecond temporal resolution and 100% prediction rate,” said Sun.

In developing their real-time keyhole detection method, the researchers also advanced the way operando synchrotron x-ray imaging can be used. Utilising machine learning, they additionally discovered two modes of keyhole oscillation.

"Our findings not only advance AM research, but they can also practically serve to expand the commercial use of LPBF for metal parts manufacturing," said professor Anthony Rollett of Carnegie Mellon University, who helped devise the new detection method.

“Porosity in metal parts remains a major hurdle for wider adoption of LPBF technique in some industries,” Sun added. “Keyhole porosity is the most challenging defect type when it comes to real-time detection using lab-scale sensors because it occurs stochastically beneath the surface. Our approach provides a viable solution for high-fidelity, high-resolution detection of keyhole pore generation that can be readily applied in many AM scenarios.”

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