In:
International Journal of Materials Research, Walter de Gruyter GmbH, Vol. 107, No. 7 ( 2016-07-14), p. 659-667
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.
Type of Medium:
Online Resource
ISSN:
2195-8556
,
1862-5282
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2016
detail.hit.zdb_id:
2232675-3
detail.hit.zdb_id:
2128058-7
detail.hit.zdb_id:
203021-4
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