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Machine learning guided design of experiments to accelerate exploration of a material extrusion process parameter space

Author

Listed:
  • Devin Young

    (University of Utah)

  • Britannia Vondrasek

    (University of Utah)

  • Michael W. Czabaj

    (University of Utah)

Abstract

Parts produced using material extrusion (MEX), a common additive manufacturing method, are often limited to non-structural applications due to sub-optimal mechanical properties, including poor interlayer fracture toughness, Gc. Gc of MEX parts depends on process parameters, but the complex relationships between process parameters and Gc are not well understood. This paper describes the use of a machine learning (ML) method using Forests with Uncertainty Estimates for Learning Sequentially (FUELS) to study the effect of five process parameters on the Gc of MEX parts. Training data for the FUELS model is collected using a modified double cantilever beam (MDCB) test, and Gc is calculated using a classical beam theory approach. The FUELS method provides guided testing by suggesting additional parameter combinations from high-uncertainty regions of the parameter space. After sequentially testing a total of 2.9% of the 2205 possible parameter combinations, there was minimal change in the non-dimensional model error, and training was concluded. Gc values collected from testing ranged 0.056 kJ/m2 to 1.774 kJ/m2. The resulting parameter space was examined to better understand how Gc evolves with changing process parameters. Among other results, extrusion temperature was shown to have a greater effect on Gc at higher print speeds. Overall, the FUELS method, paired with accelerated experimental testing, provides a useful means of quickly exploring the large MEX parameter space to establish relationships among process parameters and Gc. The methods of this study can serve as a blueprint for other studies with large parameter spaces, not just in MEX but in other manufacturing processes.

Suggested Citation

  • Devin Young & Britannia Vondrasek & Michael W. Czabaj, 2025. "Machine learning guided design of experiments to accelerate exploration of a material extrusion process parameter space," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 491-508, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02255-5
    DOI: 10.1007/s10845-023-02255-5
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    References listed on IDEAS

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    1. Ruoqing Zhu & Donglin Zeng & Michael R. Kosorok, 2015. "Reinforcement Learning Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1770-1784, December.
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    4. Jingchao Jiang & Yi Xiong & Zhiyuan Zhang & David W. Rosen, 2022. "Machine learning integrated design for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1073-1086, April.
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