Machine learning methods as applied to modelling thermal conductivity of epoxy-based composites with different fillers for aircraft

Abstract

The thermal conductivity coefficient of epoxy composites for aircraft, which are reinforced with glass fiber and filled with aerosil, γ-aminopropylaerosil, aluminum oxide, chromium oxide, respectively, was simulated. To this end, various machine learning methods were used, in particular, neural networks and boosted trees. The results obtained were found to be in good agreement with the experimental data. In particular, the correlation coefficient in the test sample was 0.99%. The prediction error of neural networks in the test samples was 0.5; 0.3; 0.2%, while that of boosted trees was 1.5; 0.9%.

Keywords

• epoxy-based composites
• fillers
• modelling
• aircraft
• aerospace applications
• machine learning