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• W4281612334 endingPage "115835" @default.
• W4281612334 startingPage "115835" @default.
• W4281612334 abstract "• The method of ANN combined with FEM was an effective complementary detection technique, the appropriate activation function in the design was chosen. • An innovative ANN have a good prediction in the constitutive model. The accuracy of ANN was effectively improved, the complex calculating time problems with the fiber-reinforced composites were solved. • Progressive damage process was efficiently predicted by the proposed ANN model . 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." @default.
• W4281612334 created "2022-06-12" @default.
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• W4281612334 date "2022-09-01" @default.
• W4281612334 modified "2023-09-30" @default.
• W4281612334 title "Strength prediction and progressive damage analysis of carbon fiber reinforced polymer-laminate with circular holes by an efficient Artificial Neural Network" @default.
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• W4281612334 doi "https://doi.org/10.1016/j.compstruct.2022.115835" @default.
• W4281612334 hasPublicationYear "2022" @default.
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