Strength prediction of nanoparticle-reinforced adhesive and hybrid joints under unaged and hygrothermal conditions using machine learning and experimental methods

This study investigates the effects of adding fullerene and single-walled carbon nanotubes (SWCNT) on the strength and durability of bonded and bonded/bolted joints, specifically for composite-to-composite (CTC) and composite-to-aluminum (CTA) substrates under three-point bending, both before and after hygrothermal aging. Samples were categorized into neat specimens, specimens with added fullerene, specimens with added SWCNT, and specimens with a combination of 50% SWCNT and 50% fullerene. Results show that the optimal nanoparticle ratio differs for bonded versus bonded/bolted joints. Nanoparticles significantly reduced degradation from hygrothermal exposure, preventing interfacial debonding and slowing strength loss. Mixed formulations improved cohesive strength and shifted failure from the adhesive interface to within the adhesive layer, enhancing joint performance and durability under both unaged and aged conditions. Furthermore, six machine learning models-ridge regression, decision tree, random forest regressor, gradient boosting regressor, support vector regression, and neural networks-were applied to predict the static strength of joints. The support vector regression and decision tree models demonstrated superior performance for bonded joints, while ridge regression and gradient boosting regressor were most effective for bonded/bolted joints. The analysis highlights that joint type, substrate, nanoparticle type and percentage, and environmental aging significantly influence adhesive performance. This study offers valuable insights into the aging and durability of bonded and dissimilar joints, providing a framework to enhance joint performance and reduce the risk of failure during operational use.