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A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process

Author

Listed:
  • Zhaochen Gu

    (University of North Texas)

  • Shashank Sharma

    (University of North Texas
    University of North Texas)

  • Daniel A. Riley

    (University of North Texas
    University of North Texas)

  • Mangesh V. Pantawane

    (University of North Texas
    University of North Texas)

  • Sameehan S. Joshi

    (University of North Texas
    University of North Texas)

  • Song Fu

    (University of North Texas)

  • Narendra B. Dahotre

    (University of North Texas
    University of North Texas)

Abstract

The primary bottlenecks faced by the laser powder bed fusion (LPBF) process is the identification of optimal process parameters to obtain high density (> 99.8%) and a good surface finish (

Suggested Citation

  • Zhaochen Gu & Shashank Sharma & Daniel A. Riley & Mangesh V. Pantawane & Sameehan S. Joshi & Song Fu & Narendra B. Dahotre, 2023. "A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3341-3363, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02004-0
    DOI: 10.1007/s10845-022-02004-0
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    References listed on IDEAS

    as
    1. Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
    2. Longwei Cheng & Kai Wang & Fugee Tsung, 2020. "A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing," IISE Transactions, Taylor & Francis Journals, vol. 53(3), pages 298-312, December.
    3. Lening Wang & Xiaoyu Chen & Daniel Henkel & Ran Jin, 2021. "Family learning: A process modeling method for cyber-additive manufacturing network," IISE Transactions, Taylor & Francis Journals, vol. 54(1), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

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