Strength prediction and progressive damage analysis of carbon fiber reinforced polymer-laminate with circular holes by an efficient Artificial Neural Network

The composite laminates with circular holes find numerous applications in aerospace, automobile manufacturing and other fields due to the design and assembly of structural components. The failure analysis of composite laminates with notches or holes is of great importance in structural applications. In this work, a finite element method (FEM) based artificial neural network (ANN) model is presented to predict the strength and progressive damage behavior of carbon fiber reinforced polymer (CFRP) laminates with holes subjected to the external loads. The activation functions in the model design are reasonably chosen. The ANN prediction results are found to be in good agreement with the simulation results, thereby confirming the accuracy of ANN model. The developed ANN model is suitable for the rapid prediction of progressive damage failure behavior of the open-hole composite laminates. The elastic deformation and the progressive damage behavior of the CFRP laminates with circular holes are predicted by the proposed ANN model, which provides a good machine learning platform with high efficiency and finds potential applications in other fields.