Abstract
This study is aimed to develop an artificial neural network (ANN)-based method for predicting the elastic modulus of Continuous SiC fiber-reinforced SiC ceramic matrix (SiC/SiC) mini-composites. The mesomechanical model with pores is established based on the microstructure of ceramic matrix mini-composites. The main factors affecting the tensile elastic modulus of mini-composites were obtained using a fidelity mesomechanical model combined with finite element analysis (FEA). Secondly, based on the main factors and the finite element simulation data, the backpropagation (BP) neural network is established to map the complex relationship between the elastic modulus and the structural parameters of the mini composite material. Finally, FEA is used to verify the rationality of the neural network. The results show that the ANN algorithm is robust in the prediction of tensile modulus of mini composite materials, and the prediction error of the neural network model using only one hidden layer and 2300 groups of data samples is less than 0.7% in the input of fiber elastic modulus, fiber volume ratio, and porosity. Therefore, the ANN model proposed in this study helps to evaluate the performance of SiC/SiC mini-composites during the design phase, shortening the design and manufacturing time.
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Acknowledgements
This work was supported by the National Science and Technology Major Project, (Y2019-I-0018-0017), State Key Laboratory of Mechanics and Control for Aerospace (Nanjing University of Aeronautics and Astronautics) (Grant No. MCMS-E-0521G01).
Funding
This work was funded by the National Science and Technology Major Project (Y2019-I-0018–0017), State Key Laboratory of Mechanics and Control for Aerospace (Nanjing University of Aeronautics and Astronautics) (Grant No. MCMS-E-0521G01).
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Tang, L., Mu, F. & Chuwei, Z. Prediction of tensile elastic modulus of SiC/SiC mini-composites with the artificial neural network. Acta Mech 234, 4733–4748 (2023). https://doi.org/10.1007/s00707-023-03640-0
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DOI: https://doi.org/10.1007/s00707-023-03640-0