Metallurgy, Vol. 58 No. 3-4, 2019.
Original scientific paper
Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
Z. M. Cai
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
H. C. Ji
orcid.org/0000-0002-1592-6362
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China; National Center for Materials Service Safety, University of Science and Technology Beijing, China; School of Mechanical Engineering, University of
W. C. Pei
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
X. M. Huang
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
W. D. Li
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
Y. M. Li
; College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
Abstract
The 3Cr23Ni8Mn3N heat-resistant steel was subjected to isothermal constant strain rate compression experiments using a Gleeble - 1 500D thermal simulator. The thermal deformation behavior in the range of deformation temperature 1 000 - 1 180 °C and strain rate 0,01 - 10 s-1 was studied. Based on experimental data, the stress-strain curves of 3Cr23Ni8Mn3N were established. And the constitutive relation of BP neural network (3 × 10 × 10 × 1) was constructed. The flow stress was predicted and compared by the ANN constitutive model. The correlation coefficient (R) is 0,999, and the average relative error (AARE) is 0,697 %. The results show that the ANN constitutive model has high accuracy for predicting the thermal deformation behavior of 3Cr23Ni8Mn3N. The model can provide a good reference value for thermal processing.
Keywords
3Cr23Ni8Mn3N; artificial neural network; constitutive model; heat - resistant; stress - strain curve
Hrčak ID:
218349
URI
Publication date:
1.7.2019.
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