A research team led by Tao Sun, Associate Professor, University of Virginia, has made new discoveries that can expand additive manufacturing in industries that rely on strong metal parts, including aerospace. The research addresses the issue of detecting the formation of keyhole pores, a major defect in laser powder bed fusion (LPBF), a common additive manufacturing technique introduced in the 1990s.
LPBF uses metal powder and lasers to 3D-print metal parts, but porosity defects remain a challenge for fatigue-sensitive applications like aircraft wings. Some porosity is associated with deep and narrow vapor depressions, which are the keyholes.
The formation and size of the keyhole is a function of laser power and scanning velocity, as well as the 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 not, however, the material solidifies around the keyhole, trapping the air pocket inside the newly formed layer of material. This makes the material more brittle.
The team developed an approach to detect the exact moment when 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 percent prediction rate,” Sun said.
In developing the keyhole-detection method, the researchers also advanced the way operando synchrotron x-ray imaging can be used.
“Our findings not only advance additive manufacturing research, but they can also practically serve to expand the commercial use of LPBF for metal parts manufacturing,” said Anthony Rollett, Professor, Carnegie Mellon University.
“Porosity in metal parts remains a major hurdle for wider adoption of the LPBF technique in some industries. 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,” Sun said. “Our approach provides a viable solution for high-fidelity, high-resolution detection of keyhole pore generation that can be readily applied in many additive manufacturing scenarios.”
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