IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i1d10.1007_s10845-023-02255-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02255-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-023-02255-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    3. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    2. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    3. Crystal T. Nguyen & Daniel J. Luckett & Anna R. Kahkoska & Grace E. Shearrer & Donna Spruijt‐Metz & Jaimie N. Davis & Michael R. Kosorok, 2020. "Estimating individualized treatment regimes from crossover designs," Biometrics, The International Biometric Society, vol. 76(3), pages 778-788, September.
    4. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    5. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
    6. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.
    7. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
    8. José A. Ferreira, 2022. "Models under which random forests perform badly; consequences for applications," Computational Statistics, Springer, vol. 37(4), pages 1839-1854, September.
    9. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    10. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    11. Chou, Yuntsai & Lin, Wei, 2024. "Blockbuster or Flop? Effects of Social Media on the Chinese Film Market," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302460, International Telecommunications Society (ITS).
    12. Liu, Yehong & Yin, Guosheng, 2020. "The Delaunay triangulation learner and its ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    13. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    14. Muhammad S. Aliero & Muhammad F. Pasha & David T. Smith & Imran Ghani & Muhammad Asif & Seung Ryul Jeong & Moveh Samuel, 2022. "Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques," Energies, MDPI, vol. 15(23), pages 1-22, December.
    15. You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
    16. Tongke Yuan & Zhifeng Sun & Shihao Ma, 2019. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection," Energies, MDPI, vol. 12(22), pages 1-20, November.
    17. Chun Fai Lui & Ahmed Maged & Min Xie, 2024. "A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3543-3558, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02255-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.