Constitutive model of 3Cr23Ni8Mn3N heat-resistant steel based on back propagation (BP) neural network (NN)
Z. M. Cai,
H. C. Ji,
W. C. Pei,
X. M. Huang,
W. D. Li,
Y. M. Li
Affiliations
Z. M. Cai
College of Mechanical Engineering, North China University of Science and Technology, Hebei, Tangshan, China
H. C. Ji
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
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.
