A comparative study at the flow behavior description of 2A14 alloy using BP-ANN and strain compensated Arrhenius model

Abstract

In this work, a back-propagation artificial neural network (BP-ANN) and Arrhenius constitutive model were used to predict the stress-strain curves of 2A14 aluminum alloy based on the results of isothermal compression tests conducted at the temperature of 648 K–723 K and the strain rate of 0.01 −10 A series of statistical analyses were introduced to compare the accuracy of predictions of the two models. The average absolute relative error (AARE) , correlation coefficient (R) , relative error and standard deviation were 0.4338%, 0.9997, 0.2384, 0.0242 by BP-ANN model and 3.06%, 0.9941, 1.7993, 2.6610 by Arrhenius constitutive model, respectively, which indicates that the trained BP-ANN model is more precise than the Arrhenius constitutive model. Then the finite element simulations were conducted under the same deformation conditions on the basis of the pure experimental results and pure BP-ANN predicted results. As a result, the stroke load curve and the distributions of the effective strain are similar, which further proves that the BP-ANN model have a good capability to predict the flow behavior of 2A14 alloy.