A machine learning based prediction model for the impact mechanical response of composite laminates considering microstructure sensitive transverse properties

Abstract The accurate and efficient prediction of impact mechanical response is crucial for safety design of composite structures. In this work, high‐fidelity representative volume elements (RVEs) with fiber, matrix and fiber/matrix interface are established, in which random fiber distributions are considered. A failure envelope under transverse loads is proposed based on computational micromechanical RVEs, and it is implemented by ABAQUS VUMAT subroutines to predict the mechanical response of composite laminates under impact loads. Based on a dataset from computational macromechanical finite element simulations, an artificial neural network model is established and trained. It is found that the random fiber distribution introduced a more obvious fluctuation to tension/compression strength than shear strength. The proposed failure criteria showed a better performance than Hashin and Tsai‐Wu criteria especially in combined compression and shear loads. An ANN model with 8 hidden layers can achieve the prediction with an acceptable coefficient of determination (R 2 ) 0.98 and loss functions of mean absolute error (MAE) 71. For certain impact loading conditions, the well trained machine learning model predicted impact contact force history within 30 min, while the FEA costs about more than 75 min on the same computer. The prediction speed is increased by over 60% for certain impact loading conditions. It is hence shown that this method provides a potential alternative for evaluation of the impact resistance of composite structures. Highlights High‐fidelity computational micromechanics analysis based on representative volume elements are performed to uncover the complex relationship between microstructure and transverse strengths of composite laminates. The microstructure dependent transverse strength criterion shows high accuracy in combined compression and shear loads compared with Hashin and Tsai‐Wu criteria. A multi‐layer artificial neural network model is established and trained for the rapid prediction of impact force history for composite laminates. The rapid prediction method is achieved with a coefficient of determination of 0.98, and the prediction speed is increased by more than 60% for certain impact loading conditions.