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Artificial Neural Network (ANN)-Based Residual Strength Prediction of Carbon Fibre Reinforced Composites (CFRCs) After Impact

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Abstract

This study presents an artificial neural network (ANN) model for predicting the residual strength of carbon fibre reinforced composites (CFRCs) after low-velocity impact. First, a finite element (FE) model was developed addressing intra-laminar damage and inter-laminar delamination, to estimate low-velocity impact (LVI) and compression-after-impact (CAI) responses of CFRCs. The FE results in terms of load–displacement curves, damage patterns, and residual strengths were found to be essentially in agreement with those by experiments. An ANN model was developed using back-propagation learning algorithm and was trained using the FE results to establish a nonlinear relationship between LVI parameters (i.e. impact energy and impactor diameter) and CFRC residual strength. Twelve sets of additional CAI simulations were carried out to validate the proposed ANN-based residual strength prediction model. A good agreement was achieved between the residual strengths predicted by ANN model and the FE results with errors less than 5%, demonstrating the effectiveness of the present ANN model. The established ANN-based model can effectively reduce the experimental costs and computational time.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors are grateful for the support from the Shanghai Pujiang Program (grant number: 19PJ1410000), the National Science Fund for Distinguished Young Scholars (grant number: 11625210), the Fund of State Key Laboratory for Strength and Vibration of Mechanical Structures (grant number: SV2019-KF-04), the Fund of National Postdoctoral Program for Innovative Talents (BX20200244), and the fellowship of China Postdoctoral Science Foundation (2020M671224).

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Authors and Affiliations

  1. School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092, P.R. China

    Bin Yang, Kunkun Fu & Yan Li

  2. State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an, 710049, P.R. China

    Kunkun Fu

  3. Department of Mechanical and Aerospace Engineering, Utah State University, Logan, UT, 84322-4130, USA

    Juhyeong Lee

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  1. Bin Yang
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Correspondence to Kunkun Fu.

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Appendix

Appendix

In this study, a minimum of two LVI/CAI tests were repeated for each impact energy to ensure the reliability of test data. Figure 18 compares CAI test reproducibility of the CFRCs subjected to initial 33 J LVI. It is clear in the Fig. 18 that two CFRCs showed similar impact damage pattern, compressive load–displacement responses, and peak compressive loads at failure.

Fig. 18
figure 18

CAI test reproducibility of the CFRCs under 33 J impact

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Yang, B., Fu, K., Lee, J. et al. Artificial Neural Network (ANN)-Based Residual Strength Prediction of Carbon Fibre Reinforced Composites (CFRCs) After Impact. Appl Compos Mater 28, 809–833 (2021). https://doi.org/10.1007/s10443-021-09891-1

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