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Application of general regression neural network in identifying interfacial parameters under mixed-mode fracture

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Abstract

It is generally accepted that the cohesive zone model is a common and highly effective method for simulating interlaminar damage in multilayer materials or structures. However, difficulties are encountered in identifying the interfacial parameters of the cohesive zone model, since it is time-consuming and expensive to experimentally obtain these parameters. Especially, some engineering materials or components under real engineering conditions cannot be measured directly. In this paper, based on the bilinear traction–separation relation of coupled mixed-mode cohesive law, a machine learning-based approach is constructed to identify the cohesive interfacial parameters by using the general regression neural network. No separate measurements are required, and seven independent interfacial parameters in mixed-mode fracture problems can be conveniently determined through machine learning-based analysis. In this case, only an experimental force–displacement curve is needed. A numerical example under a mixed-mode displacement load is considered to validate the proposed method. The results show that this machine learning-based approach provides an effective and general pathway to identify the interfacial parameters under mixed-mode fracture.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 12002256, 11772245), the Fundamental Research Funds for the Central Universities in China (No. xjh012020014), and the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2020JQ-011), the Exploration Program-Q of Natural Science Foundation in Zhejiang (Program No. LQ20A020010), the Natural Science Foundation of Jiangsu (No: BK20200246), and funded by China Postdoctoral Science Foundation (No. 2020M673374). The author Qun Li gratefully acknowledges the support of the K.C. Wong Education Foundation.

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Authors and Affiliations

  1. State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Hou Junling, Lu Xuan & Li Qun

  2. Research Institute of Xi’an Jiaotong University, Zhejiang, 311215, China

    Hou Junling

  3. Xi’an Jiaotong University Suzhou Institute, Suzhou, 215123, China

    Hou Junling

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Junling, H., Xuan, L. & Qun, L. Application of general regression neural network in identifying interfacial parameters under mixed-mode fracture. Acta Mech 233, 3909–3921 (2022). https://doi.org/10.1007/s00707-022-03296-2

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