Laser powder bed fusion

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Revealing mechanisms of processing defect mitigation in laser powder bed fusion via shaped beams using high-speed X-ray imaging

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✍️ Authors: Jiandong Yuan, Qilin Guo, Samuel J Clark

πŸ”– Topics: Additive Manufacturing, Defect Detection, Laser powder bed fusion

🏒 Organizations: University of Wisconsin, Argonne National Laboratory


The laser powder bed fusion (LPBF) process utilizing a focused Gaussian-shaped beam faces challenges, including pore formation, melt pool fluctuation and liquid spattering. While beam shaping technology has been explored as a potential approach for defect mitigation, the beam-matter interaction dynamics during melting with shaped beams remain unclear. Here, we report the direct observation of ring-shaped beam-matter interaction dynamics, including pore formation, melt pool fluctuation and liquid spattering, and unveil defect mitigation mechanisms in ring-shaped beam laser powder bed fusion process. We find that, by spatially manipulating incident laser rays, the ring-shaped beam controls keyhole morphology, thereby managing the distribution of the reflected rays. This manipulation can effectively eliminate the formation of an unstable cavity at the keyhole tip, stabilizing the keyhole and mitigating keyhole pores. This enhanced keyhole stability effectively reduces the melt pool fluctuation, the formation of liquid breakup induced spatters and liquid droplet colliding induced large spatters in the laser powder bed fusion process. Additionally, the high-energy forefront of the ring-shaped beam effectively melts the powder bed, reducing agglomeration liquid spatter in the laser powder bed fusion process. The discovered defect mitigation mechanisms may guide the design of beam shaping strategies for simultaneously increasing the quality and productivity of metal additive manufacturing.

Read more at International Journal of Machine Tools and Manufacture

Velo3D proves distributed manufacturing on a global scale

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✍️ Author: Edward Wakefield

πŸ”– Topics: Additive Manufacturing, Laser Powder Bed Fusion

🏒 Organizations: Velo3D, IMI Critical


While conventional manufacturing technology has delivered in-country products on a global basis for decades – it has often involved dedicated, high-cost production assets and personnel that lack flexibility. Supply chain issues with procurement, as well as production lag times inherent to technologies like casting, can further add to costs and delayed delivery of conventionally manufactured products.

β€œWe now have the confidence, whether it’s two weeks from now or two years from now, to print that same print file at any of these suppliers in the future,” said Zach Walton, director of technical business development at Velo3D. β€œWith the Digital Product Definition, spelled out in API20S as a collection of data required to reproduce the additively manufactured component, unchanged from the 2021 project, this demonstrated the ability to not have to requalify or redevelop – which is a big win for the O&G as well as other industries trying to deploy distributed manufacturing.” These results are an important benchmark in demonstrating that distributed manufacturing using advanced metal laser powder bed fusion (LPBF) technology is achievable in the real world.

Read more at 3D Printing Media

UVA Research Team Detects Additive Manufacturing Defects in Real-Time

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✍️ Author: Tao Sun

πŸ”– Topics: Additive Manufacturing, Machine Learning, Laser Powder Bed Fusion

🏒 Organizations: University of Virginia, Carnegie Mellon, University of Wisconsin


Introduced in the 1990s, laser powder bed fusion, or LPBF uses metal powder and lasers to 3-D 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.

β€œBy 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 way a state-of-the-art tool β€” operando synchrotron x-ray imaging β€” can be used. Utilizing machine learning, they additionally discovered two modes of keyhole oscillation.

Read more at UVA Engineering News

BMW Creates Fully Automated Production Lines for 3D Printed Car Parts

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πŸ”– Topics: Additive Manufacturing, Laser Powder Bed Fusion

🏭 Vertical: Automotive

🏒 Organizations: BMW


By utilizing systems made up of laser powder bed fusion (LPBF) platforms, combined with AI and robotics, that it has developed, the IDAM consortium can print 50,000 series parts a year, as well as 10,000 new and individual parts. Opened in 2020, BMW’s campus at Oberschleißheim has 50 3D printers for both metal and plastics. Aside from investing in a variety of 3D printing startups, including Desktop Metal and Xometry, the company also employs HP MultiJet Fusion (MJF) and EOS machines, among other brands.

Read more at 3D Print