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The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173  ∼  1473 K and strain rate range of 0.01  ∼  10 s −1 . Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient ( R ), the average absolute relative error (AARE), and the relative error ( η ). For the former,  R  and AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors ( η ) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of −39.99%  ∼  35.05% and −3.77%  ∼  16.74%. As for the former, only 16.3% of the test data set possesses  η -values within ±1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.

A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy