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On the performance of expert-augmented machine learning with limited experimental data collected from powder particle characteristics used in laser powder bed fusion

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Abstract

Laser powder bed fusion (LPBF) is a metal additive manufacturing (AM) process where a part is created layer-by-layer through the melting and solidification of powder layers. The main challenge in LPBF is quality assurance. An estimation of powder rheology/flowability, as a quality-impacting factor, is therefore necessary prior to printing. To predict powder flowability based on morphological features without cost- and time-inefficient experiments, this study explores using machine learning algorithms (ML). It focuses on a sample set of standard and off-size Ti6Al4V (Ti64) powders, aiming to (1) assess ML regressors and equation fitting for flowability prediction, using the small original dataset and (2) overcome data limitations’ impact on regression through data augmentation techniques. Results showed conventional ML regressors like support vector regression (SVR) sufficed for dynamic flowability prediction. Still, more complex targets such as shear flowability and hall flowability required innovative approaches like genetic programming (equation fitting) and synthetic minority over-sampling (SMOTE), respectively, as proposed in this work.

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Authors and Affiliations

  1. Multi-Scale Additive Manufacturing Lab, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada

    Farima Liravi & Ehsan Toyserkani

  2. AP&C, a Colibrium Additive Company, Montreal, QC, Canada

    Mahdi Habibnejad-Korayem

Authors
  1. Farima Liravi
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  2. Mahdi Habibnejad-Korayem
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  3. Ehsan Toyserkani
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Contributions

The authors confirm their contribution to the paper as follows: study conception and design: Farima Liravi, Ehsan Toyserkani, and Mahdi Habibnejad. Data collection and analysis: Farima Liravi. Interpretation of results: Farima Liravi, Ehsan Toyserkani, and Mahdi Habibnejad. Draft manuscript preparation: Farima Liravi and Ehsan Toyserkani. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Ehsan Toyserkani.

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Liravi, F., Habibnejad-Korayem, M. & Toyserkani, E. On the performance of expert-augmented machine learning with limited experimental data collected from powder particle characteristics used in laser powder bed fusion. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-14044-2

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