Integrating machine learning and digital twin for strength prediction of CFRP/aluminum adhesive joints under hygrothermal conditions

Abstract

This study investigates the application of machine learning models integrated with a digital twin (DT) framework to predict and correlate the performance of carbon fibre-reinforced polymer-to-aluminum adhesive joints subjected to hygrothermal aging. By combining experimental methods with machine learning techniques, the research aims to bridge the gap between the effects of natural and accelerated aging on adhesive joints. The joints were manufactured and then left to age naturally for a period of one to 3 years. For accelerated aging, the joints were subjected to hygrothermal conditions for a period of four to 50 days. Three-point bending tests were utilized to evaluate the performance of the joints. To evaluate natural aging periods using accelerated aging data, five machine learning algorithms were used: support vector regression (SVR), artificial neural network (ANN), linear regression, random forest regression (RF) and XGBoost. scanning electron microscopy (SEM) analyses showed that moisture absorption caused a substantial change in the surface morphology of aluminum adherends, including increased roughness and crystalline formations. The results indicated that XGBoost has provided almost perfect predictions since MSE values equal to 0 were observed at all iterations, highlighting its accuracy and reliability. In contrast, the SVR and linear regression models demonstrated much lower accuracy, with clear differences observed in their predictions. The integration of digital twin with machine learning approaches turns out to be the most efficient method of real-time adaptation of the model as well as accurate performance prediction, enhancing the durability and reliability of the composite structures. Highlights: Strength prediction of adhesive joints by using Machine learning and digital twin. SEM revealed moisture-induced changes in aluminum surface morphology. XGBoost model showed high prediction accuracy.