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Identification of Mechanical Properties of Thin-Film Elastoplastic Materials by Machine Learning

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Abstract

Nanoindentation can effectively evaluate the mechanical properties of materials in the form of bulk and coating. However, the relationship between the indentation response and the stress–strain curve of thin-film elastoplastic materials is complex and thus difficult to be elucidated using traditional physics-based, empirical or statistical models. In this study, the convolutional neural network (CNN), as a practical machine learning method, is adopted and trained to rapidly obtain the mechanical properties of thin-film elastoplastic materials using nanoindentation. The proposed method is targeted for efficiently predicting mechanical properties of thin-film materials from the applied load–penetration depth curve. Combined with the power-law model to describe the elastoplastic characteristics, a dataset comprising 228 nanoindentation cases with wide ranges of material properties is numerically simulated by ABAQUS and the corresponding results are adopted for the CNN training and validating. By addressing the important elastoplastic properties characterized by elastic modulus, yield strength, and hardening exponent, the impacts of CNN’s architecture and training epochs on the predicting performance are investigated in detail. By varying the number of convolutional layers, the influence of mechanical parameters of thin-film materials on the CNN prediction accuracy is discussed. The results show that compared with the traditional reverse algorithm, CNN can greatly reduce the computational complexity and computation time and has better prediction accuracy for the constitutive parameters of thin-film elastoplastic materials.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52175148), the Natural Science Foundation of Shaanxi Province (No. 2021KW-25), the Open Cooperation Innovation Fund of Xi'an Modern Chemistry Research Institute (No. SYJJ20210409), and the Fundamental Research Funds for the Central Universities (No. 3102018ZY015).

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Authors and Affiliations

  1. School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an, 710129, China

    Xu Long, Changheng Lu, Ziyi Shen & Yutai Su

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  1. Xu Long
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  2. Changheng Lu
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  3. Ziyi Shen
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  4. Yutai Su
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Contributions

XL contributed to methodology, funding acquisition, supervision, and writing (review and editing); CL contributed to investigation, methodology, writing (the original draft), visualization, and software; ZS provided methodology and software; YS contributed to conceptualization and writing (review and editing).

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Correspondence to Xu Long.

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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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Long, X., Lu, C., Shen, Z. et al. Identification of Mechanical Properties of Thin-Film Elastoplastic Materials by Machine Learning. Acta Mech. Solida Sin. 36, 13–21 (2023). https://doi.org/10.1007/s10338-022-00340-5

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  • DOI: https://doi.org/10.1007/s10338-022-00340-5

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