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To improve the long-term reliability of adhesively bonded joints, it is important to evaluate the degradation of adhesives on exposure to high-temperature and high-humidity environments. Further, it is important to evaluate the change in material properties, such as mechanical residual strength. Although Fourier transform infrared (FTIR) spectroscopy is widely used to investigate the chemical structures in materials, it is difficult to identify the cause of degradation due to the complex composition of adhesives. This study aimed to develop a simple method for extracting the features related to degradation using FTIR spectroscopy, and to estimate the mechanical residual strength. The surface of the adhesives after immersion exposure to water at a high temperature was analysed using FTIR spectroscopy, and their mechanical strength was measured. The correspondence between them was demonstrated using machine learning. Several linear regression methods were used and compared; the model based on Lasso regression was determined to be the most suitable for extracting features and estimating residual strength. Furthermore, it was indicated that the developed model could be used to identify areas where there is a loss of strength.

Estimating the mechanical residual strength from IR spectra using machine learning for degraded adhesives