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Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718

Author

Listed:
  • O. V. Mythreyi

    (Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India)

  • M. Rohith Srinivaas

    (Department of Metallurgical & Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India)

  • Tigga Amit Kumar

    (Gas Turbine Research Establishment Research and Development Organization, Bengaluru 560093, India)

  • R. Jayaganthan

    (Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, India)

Abstract

This research work focuses on machine-learning-assisted prediction of the corrosion behavior of laser-powder-bed-fused (LPBF) and postprocessed Inconel 718. Corrosion testing data of these specimens were collected and fit into the following machine learning algorithms: polynomial regression, support vector regression, decision tree, and extreme gradient boosting. The model performance, after hyperparameter optimization, was evaluated using a set of established metrics: R 2 , mean absolute error, and root mean square error. Among the algorithms, the extreme gradient boosting algorithm performed best in predicting the corrosion behavior, closely followed by other algorithms. Feature importance analysis was executed in order to determine the postprocessing parameters that influenced the most the corrosion behavior in Inconel 718 manufactured by LPBF.

Suggested Citation

  • O. V. Mythreyi & M. Rohith Srinivaas & Tigga Amit Kumar & R. Jayaganthan, 2021. "Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718," Data, MDPI, vol. 6(8), pages 1-16, July.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:80-:d:601214
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    References listed on IDEAS

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    1. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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