TY - JOUR
T1 - Constitutive modeling of compression behavior of TC4 tube based on modified Arrhenius and artificial neural network models
AU - Tao, Zhi Jun
AU - Yang, He
AU - Li, Heng
AU - Ma, Jun
AU - Gao, Peng Fei
N1 - Publisher Copyright:
© 2015, The Nonferrous Metals Society of China and Springer-Verlag Berlin Heidelberg.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled (LDTW) TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature, strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and 0.100 s−1. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network (ANN) model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient (R), average absolute relative error (AARE) and its variation with the deformation parameters (temperature, strain rate and strain). Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573–773 K, strain rates of 0.010–0.100 s−1 and strain of 0.04–0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s−1 and strain of 0.36–0.48. Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions, which can be used to study the compression behavior of TC4 tube at the temperature range of 573–873 K and the strain rate of 0.001–0.100 s−1. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
AB - Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled (LDTW) TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature, strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and 0.100 s−1. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network (ANN) model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient (R), average absolute relative error (AARE) and its variation with the deformation parameters (temperature, strain rate and strain). Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573–773 K, strain rates of 0.010–0.100 s−1 and strain of 0.04–0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s−1 and strain of 0.36–0.48. Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions, which can be used to study the compression behavior of TC4 tube at the temperature range of 573–873 K and the strain rate of 0.001–0.100 s−1. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
KW - Compression behavior
KW - Constitutive model
KW - Modified Arrhenius model
KW - Neural network model
KW - TC4 tube
UR - http://www.scopus.com/inward/record.url?scp=84955728534&partnerID=8YFLogxK
U2 - 10.1007/s12598-015-0620-4
DO - 10.1007/s12598-015-0620-4
M3 - 文章
AN - SCOPUS:84955728534
SN - 1001-0521
VL - 35
SP - 162
EP - 171
JO - Rare Metals
JF - Rare Metals
IS - 2
ER -