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Predictions and mechanism analyses of the fatigue strength of steel based on machine learning

  • Metals & corrosion
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Abstract

It is not completely understood fatigue strength at this time due to its complex formation mechanism. Therefore, in order to address this issue, machine learning has been used to examine the important factors involved in predicting fatigue strength. In this study, a hybrid model was proposed based on the modified bagging method by combining XGBoost and LightGBM, in which the hyperparameters of the models were optimized by a grey wolf algorithm. Moreover, an interpretable method, referred to as Shapley additive explanations (SHAP), was introduced to explain the fatigue strength predictions made by ML models. The SHAP values were calculated, and feature importance of fatigue strength by XGBoost, LightGBM and the hybrid model was discussed. The final results demonstrated that the SHAP method had major potential for interpreting fatigue strength predictions, which would provide constructive guidance for the development of antifatigue steel material in the future.

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Acknowledgements

This work was supported by the project of the National Key R&D Program “Joint Research on Advanced Technology and Application of Electric Vehicles based on China-US Cooperation” (2016YFE0102200). It was also supported by the National Natural Science Foundation of China (grant number U1864207). The authors also sincerely gave thanks to the support of Technology Innovation and Entrepreneurship Team of Hunan Province

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Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, People’s Republic of China

    Feng Yan, Kai Song, Ying Liu, Shaowei Chen & Jiayong Chen

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  1. Feng Yan
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  2. Kai Song
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  3. Ying Liu
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  4. Shaowei Chen
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Contributions

FY was involved in methodology, writing—original draft preparation, and software. KS contributed to data curation, conceptualization, and formal analysis. YL helped in visualization and investigation. SC was involved in writing—reviewing and editing, supervision, and validation. JC contributed to resources, project administration, and funding acquisition.

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Correspondence to Kai Song.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Yan, F., Song, K., Liu, Y. et al. Predictions and mechanism analyses of the fatigue strength of steel based on machine learning. J Mater Sci 55, 15334–15349 (2020). https://doi.org/10.1007/s10853-020-05091-7

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