4.6 Article

Laser-Induced Keyhole Defect Dynamics during Metal Additive Manufacturing

期刊

ADVANCED ENGINEERING MATERIALS
卷 21, 期 10, 页码 -

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adem.201900455

关键词

additive manufacturing; laser powder bed fusion; titanium; X-ray imaging

资金

  1. US Department of Energy's Office of Enagreementsergy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, CPA agreements [32035, 32037, 32038]
  2. US Department of Energy [DE-AC52-07NA27344]
  3. US Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]
  4. US DOE [DE-AC02-07CH11358]
  5. DOE Office of Science [DE-SC0012704]

向作者/读者索取更多资源

Laser powder bed fusion (LPBF) metal additive manufacturing provides distinct advantages for aerospace and biomedical applications. However, widespread industrial adoption is limited by a lack of confidence in part properties driven by an incomplete understanding of how unique process parameters relate to defect formation and ultimately mechanical properties. To address that gap, high-speed X-ray imaging is used to probe subsurface melt pool dynamics and void-formation mechanisms inaccessible to other monitoring approaches. This technique directly observes the depth and dynamic behavior of the vapor depression, also known as the keyhole depression, which is formed by recoil pressure from laser-driven metal vaporization. Also, vapor bubble formation and motion due to melt pool currents is observed, including instances of bubbles splitting before solidification into clusters of smaller voids while the material rapidly cools. Other phenomena include bubbles being formed from and then recaptured by the vapor depression, leaving no voids in the final part. Such events complicate attempts to identify defect formation using surface-sensitive process-monitoring tools. Finally, once the void defects form, they cannot be repaired by simple laser scans, without introducing new defects, thus emphasizing the importance of understanding processing parameters to develop robust defect-mitigation strategies based on experimentally validated models.

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