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Constitutive modeling of compression behavior of TC4 tube based on modified Arrhenius and artificial neural network models

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Abstract

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.

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Nos. 51275415 and 50905144), the Natural Science Basic Research Plan in Shanxi Province (No. 2011JQ6004) and the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities (No. B08040).

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Authors and Affiliations

  1. State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xian, 710072, China

    Zhi-Jun Tao, He Yang, Heng Li, Jun Ma & Peng-Fei Gao

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  1. Zhi-Jun Tao
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  2. He Yang
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  3. Heng Li
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  4. Jun Ma
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  5. Peng-Fei Gao
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Correspondence to He Yang.

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Tao, ZJ., Yang, H., Li, H. et al. Constitutive modeling of compression behavior of TC4 tube based on modified Arrhenius and artificial neural network models. Rare Met. 35, 162–171 (2016). https://doi.org/10.1007/s12598-015-0620-4

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  • DOI: https://doi.org/10.1007/s12598-015-0620-4

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