Artificial Neutral Network-based Integrity Analysis of Carbon Fiber Reinforced Polymer Composites After High-Temperature Exposure

In this paper, a non-destructive inspection (NDI) method of carbon fiber reinforced polymer (CFRP) composites is proposed for flexural residual strength based on Fourier transform infrared (FTIR) test, using an artificial neural network to predict the flexural residual strength of CFRP composites after high-temperature exposure. The thermogravimetric analysis (TGA) and microstructure results showed that the CFRP composites started to decompose at 220 degrees C. Correspondingly, after high-temperature exposure of 220 degrees C, as the exposure temperature increased, the residual strength of the composites decreased. The FTIR spectra of the CFRP composites were tested with a handheld infrared instrument. It was found that when the temperature was higher than 220 degrees C, the characteristic peak intensity of the CFRP composites decreased significantly with the increase of the exposure temperature. Then, an artificial neural network was applied to establish the relationship between the FTIR intensity and the residual strength. The maximum error of the presented ANN model was less than 5%. The residual strength of CFRP composites after high-temperature exposure could be accurately predicted by the artificial neural network based on FTIR testing results.