Porosity Prediction of Ceramic Matrix Composites Based on Random Forest

Abstract

Porosity is an important characteristic attribute of carbon fiber reinforced ceramic matrix composites (CMCs), which is closely related to material properties and directly affects the application range and prospect of composite materials. For the purpose of design optimization in Material Genetic Engineered (MGE), the porosity prediction method based on machine learning technology was proposed to provide material attribute data for propulsion material performance prediction. This study collect CMCs experimental data from papers and Materials Genome Engineering Databases. According to the composition of the materials and the characteristics of the preparation process, 9 influencing factors and 1674 experimental data were selected to establish random forest regression (RFR)and compared with support vector regression (SVR). Using R2 scores and root mean squared error as model evaluation indicators, R2 scores respectively are 0.751 and 0.927 in SVR and RFR, root mean squared error respectively are 0.0062 and 0.0019. The results of the study show that the RFR model predictions are well matched with the experimental values.