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A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing

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Abstract

To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent; hence, they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM) is used, which instead of directly using machine setting parameters to predict porosity levels of printed parts, first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure. Then, these physical, machine-independent effects are used to predict porosity levels according to “pass,” “flag,” and “fail” categories instead of focusing on quantitative pore size prediction. With six learning methods’ evaluation, PIM proved to achieve good performances with prediction error of 10\(\sim \)26%. Finally, pore-encouraging influence and pore-suppressing influence were analyzed for quality analysis.

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Acknowledgements

Thanks for valuable suggestions and experiment assistance from Dr. Aaron Stebner, Dr. Branden B. Kappes, Mr. Henry Geerlings, and Mr. Senthamilaruvi Moorthy.

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Authors and Affiliations

  1. Cognitive Robotics and AI Lab (CRAI), College of Aeronautics and Engineering, Kent State University, Kent, OH, 44240, USA

    Rui Liu

  2. Alliance for the Development of Additive Processing Technologies, Colorado School of Mines, Golden, CO, 80401, USA

    Sen Liu & Xiaoli Zhang

  3. Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, 80401, USA

    Sen Liu & Xiaoli Zhang

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  1. Rui Liu
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  2. Sen Liu
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  3. Xiaoli Zhang
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Contributions

Rui proposed the method, did the experiment, and analyzed the results. Sen analyzed the result. Xiaoli managed the whole project.

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Correspondence to Xiaoli Zhang.

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Liu, R., Liu, S. & Zhang, X. A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. Int J Adv Manuf Technol 113, 1943–1958 (2021). https://doi.org/10.1007/s00170-021-06640-3

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  • DOI: https://doi.org/10.1007/s00170-021-06640-3

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