Tao Sun (right) and Zhongshu Ren (left), a postdoctoral fellow in Sun’s research group, display a metal part printed by LPBF using their real-time keyhole detection method. Photo: Tom Cogill for UVA Engineering.A research team led by Tao Sun, associate professor of materials science and engineering at the University of Virginia, has made new discoveries that could expand additive manufacturing in aerospace and other industries that rely on strong metal parts.
In a paper in Science, the team addresses the issue of detecting the formation of keyhole pores, one of the main defects in a common additive manufacturing technique called laser powder bed fusion (LPBF). Introduced in the 1990s, LPBF uses metal powder and lasers to 3D print metal parts layer-by-layer. But porosity defects in these parts can present a challenge for fatigue-sensitive applications like aircraft wings. Some of this porosity is associated with deep and narrow vapor depressions known as keyholes.
The formation and size of a keyhole is a function of laser power and scanning velocity, as well as a materials’ capacity for absorbing laser energy. If the keyhole has stable walls, it can enhance the surrounding material’s laser absorption and improve laser manufacturing efficiency. If, however, the walls are wobbly or collapse, the material solidifies around the keyhole, trapping an air pocket inside the newly formed layer of material, making it more brittle and more likely to crack under environmental stress.
Sun and his team, including materials science and engineering professor Anthony Rollett from Carnegie Mellon University (CMU) and mechanical engineering professor Lianyi Chen from the University of Wisconsin-Madison, 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% prediction rate,” Sun said.
In developing their real-time keyhole detection method, the researchers also advanced the use of a state-of-the-art technique called operando synchrotron X-ray imaging. In addition, by utilizing machine learning, they discovered two modes of keyhole oscillation.
"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 Rollett, who is also the co-director of the NextManufacturing Center at CMU.
“Porosity in metal parts remains a major hurdle for wider adoption of LPBF technique in some industries,” said Sun. “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 additive manufacturing scenarios.”
This story is adapted from material from the University of Virginia School of Engineering and Applied Science, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.