Machine learning/finite element analysis - A collaborative approach for predicting the axial impact response of adhesively bonded joints with unique sandwich composite adherends

Abstract

Despite the increasing usage of adhesively bonded joints (ABJs) in various industries, optimization of their bond strength in a cost-effective manner remains a challenging task, particularly when complex loading scenarios such as static and dynamic compressive loadings are considered. The task becomes even more challenging in bonded joints with sandwich composite adherends. This study focuses on the performances of double-strap ABJs configured by unique sandwich composite adherends, carbon fiber-reinforced plastic straps, and a room-cured structural epoxy resin under axial impact loading. A Finite Element-Cohesive Zone (FE-CZ) model is developed to simulate the response of the joints, and its integrity is validated against the experimental tests at three impact energy levels. The model is used to simulate the response of various ABJ configurations under axial impact loading, taking into account 13 material, geometrical, and testing-related parameters that influence joint strength, thereby generating 410 data sets. Subsequently, three Machine Learning (ML) models, including deep neural networks and genetic evolution (i.e., genetic programming and genetic algorithms) are developed and trained by the data sets to predict the ABJs load-bearing capacity. The ML models explore the relationship between the design parameters and the joint's ultimate load-bearing capacities, leading to the development of cost-effective and accurate empirical equations, and optimized joint configurations.