3D-printing breakthrough mitigates three defects simultaneously for failure-free metal parts

University of Wisconsin-Madison engineers have found a way to simultaneously mitigate three types of defects in parts produced using a prominent additive manufacturing technique called laser powder bed fusion.

Led by Lianyi Chen, an associate professor of mechanical engineering at UW-Madison, the team discovered the mechanisms and identified the processing conditions that can lead to this significant reduction in defects. The researchers detailed their findings in a paper published on November 16, 2024 in the International Journal of Machine Tools and Manufacture.

“Previous research has normally focused on reducing one type of defect, but that would require the usage of other techniques to mitigate the remaining types of defects,” Chen says. “Based on the mechanisms we discovered, we developed an approach that can mitigate all the defects—pores, rough surfaces and large spatters—at once. In addition, our approach allows us to produce a part much faster without any quality compromises.”

Multiple industries, including aerospace, medical and energy, are increasingly interested in using additive manufacturing (also known as 3D printing) to produce metal parts with complex shapes that are difficult or impossible to create using conventional methods.

The researchers’ findings were published on November 16, 2024, in the International Journal of Machine Tools and Manufacture. Chen states that previous research has focused on reducing one type of defect, requiring additional techniques to address other defects. However, their approach mitigates all defects, including pores, rough surfaces, and large spatters, simultaneously. Furthermore, this approach enables faster production of high-quality parts without compromise. Various sectors, such as aerospace, medical, and energy, are increasingly interested in utilizing additive manufacturing (3D printing) for complex metal part production.

But the big challenge is that metal parts created with additive manufacturing have defects—like pores, or “voids,” rough surfaces and large spatters—that significantly compromise the finished part’s reliability and durability. These quality problems prevent 3D-printed parts from being used for critical applications where failure is not an option.

By providing a path for simultaneously increasing part quality and manufacturing productivity, the UW-Madison team’s advance could lead to widespread industry adoption of laser powder bed fusion.

Laser powder bed fusion uses a high-energy laser beam to melt and fuse thin layers of metal powder, constructing a part layer by layer from the bottom up. In this research, the UW-Madison team used an innovative ring-shaped laser beam, provided by a leading laser company called nLight, instead of the usual Gaussian-shaped beam.

The ring-shaped laser beam played a key role in this breakthrough—as did critical “in-situ” experiments, says Jiandong Yuan, the lead author of the paper and a PhD student in Chen’s group.

To see how the material behaved within the part as it was printing, researchers went to the Advanced Photon Source, an ultra-bright, high-energy synchrotron X-ray user facility at Argonne National Laboratory. Combining high-speed synchrotron X-ray imaging, theoretical analysis and numerical simulation, the researchers revealed the defect mitigation mechanisms, which involve phenomena that reduce instabilities in the laser powder bed fusion process.

The researchers also demonstrated that they could use the ring-shaped beam to drill deeper into the material without causing instabilities in the process. This enabled them to print thicker layers, increasing the manufacturing productivity. “Because we understood the underlying mechanisms, we could more quickly identify the right processing conditions to produce high-quality parts using the ring-shaped beam,” says Chen.

Lianyi Chen is the Kuo K. & Cindy F. Wang Associate Professor of mechanical engineering.

Collaborators from UW-Madison include Qilin Guo, Luis Escano, Ali Nabba, Minglei Qu, Junye Huang, Qingyuan Li, Allen Jonathan Román, and Professor Tim Osswald. Samuel Clark and Kamel Fezzaa from Argonne National Laboratory also collaborated on this project.

This work was supported by the National Science Foundation and the Wisconsin Alumni Research Foundation.