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The Scientific World Journal
Volume 2014, Article ID 108492, 12 pages
http://dx.doi.org/10.1155/2014/108492
Research Article

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

School of Material Science and Engineering, Chongqing University, Chongqing 400044, China

Received 24 August 2013; Accepted 22 December 2013; Published 12 February 2014

Academic Editors: F. Berto and Y.-Y. Chen

Copyright © 2014 Guo-zheng Quan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 11731473 K and strain rate range of 0.0110 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 ( ), the average absolute relative error (AARE), and the relative error ( ). For the former, 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 ( ) 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 -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.