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Universal scaling laws of keyhole stability and porosity in 3D printing of metals

Author

Listed:
  • Zhengtao Gan

    (Northwestern University)

  • Orion L. Kafka

    (Northwestern University
    National Institute of Standards and Technology)

  • Niranjan Parab

    (Argonne National Laboratory
    Intel Corporation)

  • Cang Zhao

    (Argonne National Laboratory
    Tsinghua University)

  • Lichao Fang

    (Northwestern University)

  • Olle Heinonen

    (Argonne National Laboratory)

  • Tao Sun

    (Argonne National Laboratory
    University of Virginia)

  • Wing Kam Liu

    (Northwestern University)

Abstract

Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.

Suggested Citation

  • Zhengtao Gan & Orion L. Kafka & Niranjan Parab & Cang Zhao & Lichao Fang & Olle Heinonen & Tao Sun & Wing Kam Liu, 2021. "Universal scaling laws of keyhole stability and porosity in 3D printing of metals," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22704-0
    DOI: 10.1038/s41467-021-22704-0
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    Cited by:

    1. Shivam Gupta & Sachin Modgil & Piera Centobelli & Roberto Cerchione & Serena Strazzullo, 2022. "Additive Manufacturing and Green Information Systems as Technological Capabilities for Firm Performance," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(4), pages 515-534, December.
    2. Milad Hamidi Nasab & Giulio Masinelli & Charlotte Formanoir & Lucas Schlenger & Steven Petegem & Reza Esmaeilzadeh & Kilian Wasmer & Ashish Ganvir & Antti Salminen & Florian Aymanns & Federica Marone , 2023. "Harmonizing sound and light: X-ray imaging unveils acoustic signatures of stochastic inter-regime instabilities during laser melting," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Yuze Huang & Tristan G. Fleming & Samuel J. Clark & Sebastian Marussi & Kamel Fezzaa & Jeyan Thiyagalingam & Chu Lun Alex Leung & Peter D. Lee, 2022. "Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. 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.
    5. Wu, Jianzhao & Zhang, Chaoyong & Giam, Amanda & Chia, Hou Yi & Cao, Huajun & Ge, Wenjun & Yan, Wentao, 2024. "Physics-assisted transfer learning metamodels to predict bead geometry and carbon emission in laser butt welding," Applied Energy, Elsevier, vol. 359(C).
    6. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.
    7. Xiaoyu Xie & Arash Samaei & Jiachen Guo & Wing Kam Liu & Zhengtao Gan, 2022. "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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