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Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting

Author

Listed:
  • Thai Le-Hong

    (Thu Dau Mot University
    Institut Polytechnique de Paris)

  • Pai Chen Lin

    (National Chung-Cheng University
    National Chung-Cheng University)

  • Jian-Zhong Chen

    (National Chung-Cheng University
    National Chung-Cheng University)

  • Thinh Duc Quy Pham

    (Thu Dau Mot University
    Université de Liège)

  • Xuan Tran

    (Thu Dau Mot University)

Abstract

In this paper, the effects of two key process parameters of the selective laser melting process, namely laser power and scanning speed, on the single-track morphologies and the bead characteristics, especially the depth-to-width D/W and height-to-width H/W ratios, were investigated using both experimental and Machine Learning (ML) approaches. A total of 840 single tracks were fabricated with several combinations of laser power and scanning speed levels. Surface morphologies of the single tracks and bead profiles were thoroughly investigated, providing a track-type map and the evolutions of the bead characteristics as a function of laser power and scanning speed. The results indicate neither severe balling nor keyholing effect for all combinations of laser power and scanning speed. Besides, simple relationships cannot accurately describe the evolutions of the D/W and H/W ratios as a function of laser power and scanning speed. Two Machine Learning-based regression models, Random Forest and Artificial Neural Network, were chosen to estimate the D/W and H/W ratios using laser power and scanning speed. The Bayesian optimization algorithm was employed to optimize the model hyperparameter selection. Both Machine Learning-based models appear to be able to predict reasonably well the two aspect ratios, D/W and H/W, with an overall R2 value reaching about 90%, evaluated on the cross-validation dataset after a few seconds of training time, respectively.

Suggested Citation

  • Thai Le-Hong & Pai Chen Lin & Jian-Zhong Chen & Thinh Duc Quy Pham & Xuan Tran, 2023. "Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1241-1257, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01845-5
    DOI: 10.1007/s10845-021-01845-5
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    References listed on IDEAS

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    1. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    2. Mojtaba Khanzadeh & Sudipta Chowdhury & Mark A. Tschopp & Haley R. Doude & Mohammad Marufuzzaman & Linkan Bian, 2019. "In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes," IISE Transactions, Taylor & Francis Journals, vol. 51(5), pages 437-455, May.
    3. A. Garg & Jasmine Siu Lee Lam & M. M. Savalani, 2018. "Laser power based surface characteristics models for 3-D printing process," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1191-1202, August.
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