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JIAN Jie, GUO Biao, LI Qiang, LI Xiao, SONG Jiupeng, ZHANG Yu, AO Jinqing, HUANG Yong. Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels[J]. Powder Metallurgy Technology, 2023, 41(6): 577-585. DOI: 10.19591/j.cnki.cn11-1974/tf.2021080001
Citation: JIAN Jie, GUO Biao, LI Qiang, LI Xiao, SONG Jiupeng, ZHANG Yu, AO Jinqing, HUANG Yong. Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels[J]. Powder Metallurgy Technology, 2023, 41(6): 577-585. DOI: 10.19591/j.cnki.cn11-1974/tf.2021080001

Elevated temperature tensile deformation behavior of powder metallurgy Fe−2Cu−0.5C steels

  • The elevated temperature tensile tests of the powder metallurgy (P/M) Fe−2Cu−0.5C steels at the deformation temperatures of 850~1000 ℃ and strain rates of 0.1~10.0 s−1 were carried out by Gleeble-3500 thermal simulator. The effects of deformation temperature and strain rate on the elevated temperature tensile deformation behaviors of the P/M steels were quantitatively analyzed. The fracture work of the P/M steels during the elevated temperature tensile was calculated using the stress-strain curves. The mathematical model characterizing the relationship of fracture work, deformation temperature, and strain rate was established. The Hensel-Spittel model and BP neural network model were used to establish the constitutive equation of the P/M steels to characterize the flow behaviors during the elevated temperature tensile. The predicted results of the two models were compared. The results show that the fracture work model can describe the capacity of the P/M steels to resist fracture at different deformation temperatures and strain rates. The average absolute relative error between the predicted values of Hensel-Spittel constitutive model and the experimental values is 3.16%, and the coefficient of determination is 0.9743. While the average absolute relative error and the coefficient of determination between the predicted values of BP neural network model and the experimental values are 0.17% and 0.9999, respectively, indicating that the BP neural network model has the stronger predictive capacity and can characterize the elevated temperature tensile deformation behaviors of the P/M Fe−2Cu−0.5C steels better.
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