Accessible Unlicensed Requires Authentication Published by De Gruyter August 31, 2016

Comparing predictions from constitutive equations and artificial neural network model of compressive behavior in carbon nanotube–aluminum reinforced ZA27 composites

Yang Liu, Yunke Zhu, Cong Geng and Junrui Xu

Abstract

Hybrid carbon nanotube–aluminum reinforced ZA27 composites under hot compressive forces were investigated in the temperature range of 473 – 523 K with strain rates of 0.01 – 10 s−1. From the experimental data, the flow stress curves for increasing strain exhibit typical flow behavior associated with dynamic recrystallization softening. A comparison of predictions from an artificial neural network model and the constitutive equations to describe the hot compressive behavior was performed. Relative errors varied from −4.14 % to 6.75 % for the artificial neural network model and from −15.93 % to 17.29 % using the constitutive equations. The results indicate that the artificial neural network model was more accurate and efficient in predicting hot compressive behavior.


*Correspondence address, Dr. Yang Liu, School of Mechanical Engineering, Xiangtan University, 411105 Xiangtan, China, Tel.: +86 73158292454, Fax: +86 73158292454, E-mail:

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Received: 2015-11-05
Accepted: 2016-03-23
Published Online: 2016-08-31
Published in Print: 2016-07-14

© 2016, Carl Hanser Verlag, München