A Flow Stress Equation of AA5005 Aluminum Alloy Based on Fields-Backofen Model

Article Preview

Abstract:

Tensile tests on AA5005 alloy were conducted on model MTS-810 tensile test machine during temperature 633-773 K and strain rate 0.0003-0.03 s-1. The flow stress–true strain curves were obtained. In order to analyze the flow stress behavior of aluminum AA5005 alloy, the phenomenological Fields-Backofen equation based on the fitting regression analysis was developed. The flow stress values calculated by the obtained model keep coincidence with experimental values. Eventually, the statistical analysis methods (correlation coefficient (R), average absolute relative error (AARE)) were adopted to examine the credibility of the established model. Results show that the R-value is 0.99592 and the AARE is 3.3128 %, which indicates the high fitting accuracy of the Fields-Backofen equation. Consequently, the Fields-Backofen model can describe the constitutive relationship of AA5005 alloy credibly.

You might also be interested in these eBooks

Info:

Periodical:

Materials Science Forum (Volume 1078)

Pages:

3-10

Citation:

Online since:

December 2022

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2022 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] W. S. Miller et al., Recent development in aluminium alloys for the automotive industry,, Mater. Sci. Eng. A, vol. 280, no. 1, p.37–49, Mar. 2000,.

Google Scholar

[2] A. Jenab, I. Sari Sarraf, D. E. Green, T. Rahmaan, and M. J. Worswick, The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-Osheets,, Mater. Des., vol. 94, p.262–273, Mar. 2016,.

DOI: 10.1016/j.matdes.2016.01.038

Google Scholar

[3] S. J. Li, W. N. Chen, B. Krishna Singh, N. Kosimov, and D. W. Jung, Study on Flow Stress Model of AA5005 Material,, Solid State Phenom., vol. 335, p.107–112, 2022,.

DOI: 10.4028/p-4t00fs

Google Scholar

[4] S. Li, W. Chen, K. S. Bhandari, D. W. Jung, and X. Chen, Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model,, Materials, vol. 15, no. 11, p.3788, May 2022,.

DOI: 10.3390/ma15113788

Google Scholar

[5] F. Yin, L. Hua, H. Mao, X. Han, D. Qian, and R. Zhang, Microstructural modeling and simulation for GCr15 steel during elevated temperature deformation,, Mater. Des., vol. 55, p.560–573, Mar. 2014,.

DOI: 10.1016/j.matdes.2013.10.042

Google Scholar

[6] W. N. Chen, S. J. Li, N. Kosimov, B. Krishna Singh, and D. W. Jung, Research on High-Temperature Constitutive Relationship of Aluminum Alloy,, Solid State Phenom., vol. 335, p.101–106, 2022,.

DOI: 10.4028/p-zr45qd

Google Scholar

[7] M. Murugesan and D. W. Jung, Johnson Cook Material and Failure Model Parameters Estimation of AISI-1045 Medium Carbon Steel for Metal Forming Applications,, Materials, vol. 12, no. 4, Art. no. 4, Jan. 2019,.

DOI: 10.3390/ma12040609

Google Scholar

[8] W. Chen, S. Li, S. Aziz, K. S. Bhandari, X. Chen, and D.-W. Jung, Flow Behavior Modeling Optimization and Activation Energy Analysis of Al-Mg Alloy Aided by Genetic Algorithm., Rochester, NY, Oct. 05, 2022. Accessed: Nov. 06, 2022. [Online]. Available: https://papers.ssrn.com/abstract=4235225.

DOI: 10.2139/ssrn.4235225

Google Scholar

[9] Y. Q. Cheng, H. Zhang, Z. H. Chen, and K. F. Xian, Flow stress equation of AZ31 magnesium alloy sheet during warm tensile deformation,, J. Mater. Process. Technol., vol. 208, no. 1–3, p.29–34, Nov. 2008,.

DOI: 10.1016/j.jmatprotec.2007.12.095

Google Scholar

[10] N. Kotkunde, H. N. Krishnamurthy, S. K. Singh, and G. Jella, Experimental and Numerical Investigations on Hot Deformation Behavior and Processing Maps for ASS 304 and ASS 316,, High Temp. Mater. Process., vol. 37, no. 9–10, p.873–888, Oct. 2018,.

DOI: 10.1515/htmp-2017-0047

Google Scholar

[11] H. R. R. Ashtiani and A. A. Shayanpoor, New constitutive equation utilizing grain size for modeling of hot deformation behavior of AA1070 aluminum,, Trans. Nonferrous Met. Soc. China, vol. 31, no. 2, p.345–357, Feb. 2021,.

DOI: 10.1016/s1003-6326(21)65500-0

Google Scholar

[12] G. Ji, F. Li, Q. Li, H. Li, and Z. Li, A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel,, Mater. Sci. Eng. A, vol. 528, no. 13, p.4774–4782, May 2011,.

DOI: 10.1016/j.msea.2011.03.017

Google Scholar