Mechanical properties prediction of composite laminate with FEA and machine learning coupled method

In order to predict mechanical properties of composite laminate, a method coupling finite element analysis (FEA) and machine learning is established to analyze three examples of composite laminates, such as failure factor of Puck theory under random stress state, failure factor and critical buckling eigenvalues of open-hole laminate. By writing Abaqus script, parametric FEA models of composite laminates are built and large samples are generated. Artificial neural network (ANN) model and random forest (RF) model are set up to learn these virtual samples to predict their corresponding mechanical properties for these examples. By analyzing predicted results and FEA values of these mechanical parameters, predicted data are well consistent with the curves of FEA values and the calculated root-mean-square errors are much smaller, which proves this FEA and machine learning coupled method is effective. Even if these errors predicted by ANN model are smaller than RF model, and the learning processes of RF model take less time, these methods with ANN model and RF model are all appropriate to predict mechanical properties of composite laminates.

Automated summary

A method combining finite element analysis (FEA) and machine learning is studied to predict mechanical properties of composite laminates, by establishing FEA models and using artificial neural network (ANN) and random forest (RF) models to learn virtual samples, predicting mechanical properties. The predicted results are consistent with FEA values, demonstrating the effectiveness of this method.