TY - JOUR
T1 - Modeling constitutive relationship of Ti17 titanium alloy with lamellar starting microstructure
AU - Ma, Xiong
AU - Zeng, Weidong
AU - Sun, Yu
AU - Wang, Kaixuan
AU - Lai, Yunjin
AU - Zhou, Yigang
PY - 2012/3/15
Y1 - 2012/3/15
N2 - The isothermal compression tests of Ti17 titanium alloy with lamellar starting microstructure were conducted on a Gleeble-1500 thermo-mechanical simulator at the deformation temperatures ranging from 780 to 860°C with an interval of 20°C and the strain rates of 0.001, 0.01, 0.1, 1.0 and 10.0s-1 with the height reduction of 40 and 60%. The typical flow curves exhibit softening at all the deformation conditions, even at low strain rate (0.001s-1), which have been considered that the flow softening results from adiabatic shear bands at high strain rates and lamellar globularization at low strain rates. On the basis of the experimental data, the artificial neural network model was proposed to develop the constitutive relationship of Ti17 alloy with lamellar starting microstructure. In the present investigation, the input parameters of ANN model are strain, strain rate and deformation temperature. The output parameter of ANN model is the flow stress. The comparison of experimental flow stresses with predicted value by ANN model and calculated value by regression model was carried out. It is found that the predicted flow stresses obtained from ANN were in a better agreement with the experimental values, indicating that it is available and novel to establish the constitutive relationship of Ti17 alloy using the technique of artificial neural network.
AB - The isothermal compression tests of Ti17 titanium alloy with lamellar starting microstructure were conducted on a Gleeble-1500 thermo-mechanical simulator at the deformation temperatures ranging from 780 to 860°C with an interval of 20°C and the strain rates of 0.001, 0.01, 0.1, 1.0 and 10.0s-1 with the height reduction of 40 and 60%. The typical flow curves exhibit softening at all the deformation conditions, even at low strain rate (0.001s-1), which have been considered that the flow softening results from adiabatic shear bands at high strain rates and lamellar globularization at low strain rates. On the basis of the experimental data, the artificial neural network model was proposed to develop the constitutive relationship of Ti17 alloy with lamellar starting microstructure. In the present investigation, the input parameters of ANN model are strain, strain rate and deformation temperature. The output parameter of ANN model is the flow stress. The comparison of experimental flow stresses with predicted value by ANN model and calculated value by regression model was carried out. It is found that the predicted flow stresses obtained from ANN were in a better agreement with the experimental values, indicating that it is available and novel to establish the constitutive relationship of Ti17 alloy using the technique of artificial neural network.
KW - Artificial neural network
KW - Constitutive relationship
KW - Lamellar starting microstructure
KW - Regression method
KW - Ti17 titanium alloy
UR - http://www.scopus.com/inward/record.url?scp=84862830035&partnerID=8YFLogxK
U2 - 10.1016/j.msea.2012.01.027
DO - 10.1016/j.msea.2012.01.027
M3 - 文章
AN - SCOPUS:84862830035
SN - 0921-5093
VL - 538
SP - 182
EP - 189
JO - Materials Science and Engineering: A
JF - Materials Science and Engineering: A
ER -