Thermal degradation detection of cured epoxy resins and composites is currently limited to severe thermal damage in practice. Evaluating the change in mechanical properties after a short-time thermal exposure, as well as estimating the history of thermally degraded polymers, has remained a challenge until now. An approach to accurately predict the mechanical properties, as well as the thermal exposure time and temperature of epoxy resin, using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy, data processing, and artificial neural networks, is presented here. Therefore, an epoxy resin has been fully cured and exposed to elevated temperatures for different time periods. A FTIR-spectrometer was used to measure molecular changes, using mid-IR (MIR)-FTIR for film samples and near-IR (NIR)-FTIR for bulk samples. A quantitative analysis of the thermally degraded film samples shows oxidation, chain-scission, and dehydration in the FTIR spectra in the MIR-range. Using NIR spectroscopy for the bulk samples, only minor changes in the FTIR spectra could be detected. However, using data processing, molecular information was extracted from the NIR range and a degradation model, using an artificial neural network, has been trained. Even though the changes due to thermal exposure were small, the presented model is capable of accurately predicting the time, temperature, and residual strength of the polymer.
Keywords: FTIR; artificial neural network; machine learning; non-destructive testing; thermal aging.