Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning

Abstract Carbon fiber is the most common reinforcing phase in composite materials. However, it is difficult to obtain the performance parameters of the monofilament . In this study, the relationship between the property variables of the carbon fiber monofilament and the macroscopic parameters of the composites is established using a regression tree, a type of decision tree model, in machine learning. First, in order to obtain the data for machine learning, representative volume element (RVE) models of single-layer and multi-layer carbon fiber reinforced plastic (CFRP) are established by a cross-scale finite element method (FEM), and periodic boundary conditions are loaded. Then, a correlation model between the carbon fiber properties and CFRP and matrix properties is established. The non-GUI mode is called by Software Isight to generate the sample data. Second, in order to avoid overfitting, the L1 norm method is used for feature selection before model training. Finally, the four elastic properties of the carbon fiber are analyzed by a regression tree model. After a series of parameter adjustments and model selection, the model with a better generalization performance was obtained. The validity of the models was verified by the validating sample set.