Revealing melt flow instabilities in laser powder bed fusion additive manufacturing of aluminum alloy via in-situ high-speed X-ray imaging

Guo, Qilin; Qu, Minglei; Escano, Luis I.; Hojjatzadeh, S. H.; Young, Zachary; Fezzaa, Kamel; Chen, Lianyi

doi:10.1016/j.ijmachtools.2022.103861

Title: Revealing melt flow instabilities in laser powder bed fusion additive manufacturing of aluminum alloy via in-situ high-speed X-ray imaging

Journal Article · Sat Feb 12 00:00:00 EST 2022 · International Journal of Machine Tools and Manufacture

DOI:https://doi.org/10.1016/j.ijmachtools.2022.103861· OSTI ID:1970366

Guo, Qilin ^[1]; Qu, Minglei ^[1]; Escano, Luis I. ^[1]; Hojjatzadeh, S. H. ^[1]; Young, Zachary ^[1]; Fezzaa, Kamel ^[2]; Chen, Lianyi ^[1]

University of Wisconsin, Madison, WI (United States)
Argonne National Laboratory (ANL), Lemont, IL (United States)

Laser metal additive manufacturing technologies enable the fabrication of geometrically and compositionally complex parts unachievable by conventional manufacturing methods. However, the certification and qualification of additively manufactured parts are greatly hindered by the stochastic melt flow instabilities intrinsic to the process, which has not been explicitly revealed by direct observation. Here, we report the mechanisms of the melt flow instabilities in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-resolution synchrotron X-ray imaging. Here we identified powder/droplet impact, significant keyhole oscillation, and melting-mode switching as three major mechanisms for causing melt flow instabilities. We demonstrated the detrimental consequences of these instabilities brought to the process, and presented new understanding on the melt flow evolution and keyhole oscillation. This work provides critical insights into process instabilities during laser metal additive manufacturing, which may guide the development of instability mitigation approaches. The results reported here are also important for the development and validation of high-fidelity computational models.

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

Grant/Contract Number:: AC02-06CH11357; DMR-1720415

OSTI ID:: 1970366

Journal Information:: International Journal of Machine Tools and Manufacture, Vol. 175; ISSN 0890-6955

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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