Identification of component material in-situ properties of C/SiC composites based on self-consistent clustering analysis and Bayesian method

Abstract

In the paper, a method for identifying the mechanical properties of the in-situ component materials in carbon fiber reinforced silicon carbide ceramic matrix composites based on the macro mechanical test data is proposed. Firstly, the computation efficiency of considering damage behavior in the meso-mechanial model is improved through the self-consistent clustering analysis. Subsequently, sensitivity analysis is introduced in the parameter identification based on the Bayesian network to reduce the number of parameters to be identified simultaneously, thereby alleviating the ill-posedness of the inverse problem. Numerical and experimental cases were conducted to validate the proposed method. The maximum error of parameter identification is 6.0 % and the prediction error for strength is only 1.7 % in the numerical case with 5 % Gaussian noise. In the experimental case, the stress–strain curve calculated using the identified results shows good agreement with the experimental data. The prediction error for strength is only 2.2 %, while the maximum deviation between the identified results and the reference value in the literature can be up to 50 %, indicating the importance of obtaining the properties of component materials in-situ.