Neural networks as material models within a multiscale approach
Computers and Structures
Integration of feedforward neural network and finite element in the draw-bend springback prediction
Expert Systems with Applications: An International Journal
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This paper describes an approach based on artificial neural network (ANN) to identify the material flow curves of strain hardened 5083-H111 and 5754-O Al-Mg alloys at the temperature ranges from room temperature (RT) to 300^oC and a strain rate of 0.0016-0.16s^-^1. The tensile tests were performed to determine the material responses at various temperatures and strain rates. An ANN model was developed to predict the flow curves of the materials in terms of experimental data. The input parameters of the model are strain rate, temperature, and strain while tensile flow stress is the output. A three layer feed-forward network was trained with BFGS (Broyden, Fletcher, Goldfarb, and Shanno) algorithm. The amount of the neurons in the hidden layer was determined by determining of the root mean square error (RMSE) values for each material. Results reveal that the predicted values in the ANN model are in very good agreement with the experimental data. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of 5083-H111 and 5754-O Al-Mg alloys for tensile test at prescribed deformation conditions.