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Controlling process instability for defect lean metal additive manufacturing

Author

Listed:
  • Minglei Qu

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Qilin Guo

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Luis I. Escano

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Ali Nabaa

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • S. Mohammad H. Hojjatzadeh

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Zachary A. Young

    (University of Wisconsin-Madison)

  • Lianyi Chen

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

Abstract

The process instabilities intrinsic to the localized laser-powder bed interaction cause the formation of various defects in laser powder bed fusion (LPBF) additive manufacturing process. Particularly, the stochastic formation of large spatters leads to unpredictable defects in the as-printed parts. Here we report the elimination of large spatters through controlling laser-powder bed interaction instabilities by using nanoparticles. The elimination of large spatters results in 3D printing of defect lean sample with good consistency and enhanced properties. We reveal that two mechanisms work synergistically to eliminate all types of large spatters: (1) nanoparticle-enabled control of molten pool fluctuation eliminates the liquid breakup induced large spatters; (2) nanoparticle-enabled control of the liquid droplet coalescence eliminates liquid droplet colliding induced large spatters. The nanoparticle-enabled simultaneous stabilization of molten pool fluctuation and prevention of liquid droplet coalescence discovered here provide a potential way to achieve defect lean metal additive manufacturing.

Suggested Citation

  • Minglei Qu & Qilin Guo & Luis I. Escano & Ali Nabaa & S. Mohammad H. Hojjatzadeh & Zachary A. Young & Lianyi Chen, 2022. "Controlling process instability for defect lean metal additive manufacturing," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28649-2
    DOI: 10.1038/s41467-022-28649-2
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    Cited by:

    1. Wang, Haijie & Li, Bo & Lei, Liming & Xuan, Fuzhen, 2024. "Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Kai Zhang & Yunhui Chen & Sebastian Marussi & Xianqiang Fan & Maureen Fitzpatrick & Shishira Bhagavath & Marta Majkut & Bratislav Lukic & Kudakwashe Jakata & Alexander Rack & Martyn A. Jones & Junji S, 2024. "Pore evolution mechanisms during directed energy deposition additive manufacturing," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Dongsheng Zhang & Wei Liu & Yuxiao Li & Darui Sun & Yu Wu & Shengnian Luo & Sen Chen & Ye Tao & Bingbing Zhang, 2023. "In situ observation of crystal rotation in Ni-based superalloy during additive manufacturing process," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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