TY - JOUR
T1 - Hot Deformation Behavior and Flow Stress Prediction of TC4-DT Alloy in Single-Phase Region and Dual-Phase Regions
AU - Liu, Jianglin
AU - Zeng, Weidong
AU - Zhu, Yanchun
AU - Yu, Hanqing
AU - Zhao, Yongqing
N1 - Publisher Copyright:
© 2015, ASM International.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - Isothermal compression tests of TC4-DT titanium alloy at the deformation temperature ranging from 1181 to 1341 K covering α + β phase field and β-phase field, the strain rate ranging from 0.01 to 10.0 s−1 and the height reduction of 70% were conducted on a Gleeble-3500 thermo-mechanical simulator. The experimental true stress-true strain data were employed to develop the strain-compensated Arrhenius-type flow stress model and artificial neural network (ANN) model; the predictability of two models was quantified in terms of correlation coefficient (R) and average absolute relative error (AARE). The R and AARE for the Arrhenius-type flow stress model were 0.9952 and 5.78%, which were poorer linear relation and more deviation than 0.9997 and 1.04% for the feed-forward back-propagation ANN model, respectively. The results indicated that the trained ANN model was more efficient and accurate in predicting the flow behavior for TC4-DT titanium alloy at elevated temperature deformation than the strain-compensated Arrhenius-type constitutive equations. The constitutive relationship compensating strain could track the experimental data across the whole hot working domain other than that at high strain rates (≥1 s−1). The microstructure analysis illustrated that the deformation mechanisms existed at low strain rates (≤0.1 s−1), where dynamic recrystallization occurred, were far different from that at high strain rates (≥1 s−1) that presented bands of flow localization and cracking along grain boundary.
AB - Isothermal compression tests of TC4-DT titanium alloy at the deformation temperature ranging from 1181 to 1341 K covering α + β phase field and β-phase field, the strain rate ranging from 0.01 to 10.0 s−1 and the height reduction of 70% were conducted on a Gleeble-3500 thermo-mechanical simulator. The experimental true stress-true strain data were employed to develop the strain-compensated Arrhenius-type flow stress model and artificial neural network (ANN) model; the predictability of two models was quantified in terms of correlation coefficient (R) and average absolute relative error (AARE). The R and AARE for the Arrhenius-type flow stress model were 0.9952 and 5.78%, which were poorer linear relation and more deviation than 0.9997 and 1.04% for the feed-forward back-propagation ANN model, respectively. The results indicated that the trained ANN model was more efficient and accurate in predicting the flow behavior for TC4-DT titanium alloy at elevated temperature deformation than the strain-compensated Arrhenius-type constitutive equations. The constitutive relationship compensating strain could track the experimental data across the whole hot working domain other than that at high strain rates (≥1 s−1). The microstructure analysis illustrated that the deformation mechanisms existed at low strain rates (≤0.1 s−1), where dynamic recrystallization occurred, were far different from that at high strain rates (≥1 s−1) that presented bands of flow localization and cracking along grain boundary.
KW - BP neural network
KW - constitutive relationship
KW - deformation behavior
KW - TC4-DT titanium alloy
UR - http://www.scopus.com/inward/record.url?scp=84939960572&partnerID=8YFLogxK
U2 - 10.1007/s11665-015-1456-7
DO - 10.1007/s11665-015-1456-7
M3 - 文章
AN - SCOPUS:84939960572
SN - 1059-9495
VL - 24
SP - 2140
EP - 2150
JO - Journal of Materials Engineering and Performance
JF - Journal of Materials Engineering and Performance
IS - 5
ER -