Accessible 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
From the journal International Journal of Materials Research
https://doi.org/10.3139/146.111388

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.

Keywords: Zinc–aluminum matrix composites; Carbon nanotubes; Flow behavior; Artificial neural network; Constitutive equation
*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

From the journal
International Journal of Materials Research
International Journal of Materials Research
Volume 107 Issue 7

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