Determination of cohesive parameters for fibre-reinforced composite interfaces based on finite element analysis and machine learning

Abstract

This paper proposed a method for determining the cohesive parameters of fibre-reinforced composite interfaces based on finite element analysis (FEA) and machine learning. 3D FEA models with different boundary conditions and 2D FEA models were created to simulate the process of microdroplet tests, and to compare their maximum reaction forces and the time costs. The proper FEA model that is accurate and efficient was adopted to establish the data set of machine learning. Machine learning based on the FEA data set was divided into two steps: feature selection and kernel ridge regression (KRR) prediction. Feature selection was carried out to confirm the validity of the features, to obtain the optimal parameter of KRR prediction and to quantitatively illustrate the effect of the cohesive parameters on the maximum reaction force. Interfacial shear strength (IFSS) and interfacial fracture toughness (IFFT) of a poly ( p-phenylene benzobisoxazole) (PBO) fibre-reinforced epoxy composite were successfully predicted by the KRR method without extra mechanical theories or assumptions.

A review on machine learning implementation for predicting and optimizing the mechanical behaviour of laminated fiber-reinforced polymer composites

“…Similarly, Yuan et al [ 31 ] introduced an alternative method for determining cohesive parameters from microdroplet experiments through the use of ML techniques rather than relying on traditional analytical methods. This approach involved assembling a dataset through the conduct of 120 experiments and 12,768 2D FEA simulations utilizing cohesive elements at the fiber-resin interface.…”