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Licensed 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

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