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Published in: Physics of Metals and Metallography 13/2021

01-12-2021 | STRENGTH AND PLASTICITY

Processing Workability and Artificial Neural Network of AA1070 to the Prediction of Hot Flow Stress

Authors: H. R. Rezaei Ashtiani, A. A. Shayanpoor

Published in: Physics of Metals and Metallography | Issue 13/2021

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Abstract

The hot compression flow behavior of the AA1070 aluminum was investigated at the range of deformation temperatures of 623–773 K and strain rates of 0.005–0.5 s–1. A proper artificial neural network (ANN) model was developed and employed for the prediction of the hot deformation behavior of AA1070 aluminum using the experimental results. Then accuracy and predictability of this model were evaluated in terms of correlation coefficient (R), relative error, and average absolute relative error (AARE). We show that the developed ANN model is rather efficient and accurate for predicting the flow stress of this alloy. According to the experimental data, the theory of dynamic material model (DMM) was employed to describe the workability and establish the optimum parameters of the flow stress behavior of this material. Processing maps (PMs) were obtained from superimposed instability and power dissipation maps and evaluated with microstructures of the deformed sample. The softening mechanism of homogenized AA1070 aluminum was dynamic recovery (DRV) at the lower temperature and strain rate. In the strain of 0.6, the processing map was separated into three characterization zones to investigate the microstructural evolution. Finally, the optimal domains of hot working were distinguished in the areas with a deformation temperature range of 623–773 K and strain rate range of 0.01–0.06 s–1 with fine grain structure.
Literature
1.
go back to reference G. E. Totten and D. S. MacKenzie, Handbook of Aluminum, Vol. 1: Physical Metallurgy and Processes (CRC Press, Boca Raton, FL, 2004). G. E. Totten and D. S. MacKenzie, Handbook of Aluminum, Vol. 1: Physical Metallurgy and Processes (CRC Press, Boca Raton, FL, 2004).
2.
go back to reference R. K. Roy and S. Das, “New combination of polishing and etching technique for revealing grain structure of an annealed aluminum (AA1235) alloy,” J. Mater. Sci. 41, 289–292 (2006). CrossRef R. K. Roy and S. Das, “New combination of polishing and etching technique for revealing grain structure of an annealed aluminum (AA1235) alloy,” J. Mater. Sci. 41, 289–292 (2006). CrossRef
3.
go back to reference Y. C. Lin, M. S. Chen, and J. Zhong, “Prediction of 42CrMo steel flow stress at high temperature and strain rate,” Mech. Res. Commun. 35, 142–150 (2008). CrossRef Y. C. Lin, M. S. Chen, and J. Zhong, “Prediction of 42CrMo steel flow stress at high temperature and strain rate,” Mech. Res. Commun. 35, 142–150 (2008). CrossRef
4.
go back to reference H. R. Rezaei Ashtiani, M. H. Parsa, and H. Bisadi, “Constitutive equations for elevated temperature flow behavior of commercial purity aluminum,” Mater. Sci. Eng., A 545, 61–67 (2012). CrossRef H. R. Rezaei Ashtiani, M. H. Parsa, and H. Bisadi, “Constitutive equations for elevated temperature flow behavior of commercial purity aluminum,” Mater. Sci. Eng., A 545, 61–67 (2012). CrossRef
5.
go back to reference Y. C. Lin and X. M. Chen, “A critical review of experimental results and constitutive descriptions for metals and alloys in hot working,” Mater. Des. 32, 1733–1759 (2011). CrossRef Y. C. Lin and X. M. Chen, “A critical review of experimental results and constitutive descriptions for metals and alloys in hot working,” Mater. Des. 32, 1733–1759 (2011). CrossRef
6.
go back to reference H. R. Rezaei 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 31, 345–357 (2021). CrossRef H. R. Rezaei 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 31, 345–357 (2021). CrossRef
7.
go back to reference A. Yu. Churyumov and V. V. Teleshov, “Quantitative description of the flow-stress dependence of aluminum alloys at the stage of steady flow upon hot deformation on the Zener–Hollomon parameter,” Phys. Met. Metallogr. 118, 905–912 (2017). CrossRef A. Yu. Churyumov and V. V. Teleshov, “Quantitative description of the flow-stress dependence of aluminum alloys at the stage of steady flow upon hot deformation on the Zener–Hollomon parameter,” Phys. Met. Metallogr. 118, 905–912 (2017). CrossRef
8.
go back to reference K. Li, Q. Pan, R. Li, S. Liu, Z. Huang, and X. He, “Constitutive modeling of the hot deformation behavior in 6082 aluminum alloy,” J. Mater. Eng. Perform. 28, 981–994 (2019). CrossRef K. Li, Q. Pan, R. Li, S. Liu, Z. Huang, and X. He, “Constitutive modeling of the hot deformation behavior in 6082 aluminum alloy,” J. Mater. Eng. Perform. 28, 981–994 (2019). CrossRef
9.
go back to reference S. Sani Aliakbari, G. R. Ebrahimi, H. Vafaeenezhad, and A. R. Kiani-Rashid, “Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model,” J. Magnesium Alloys 6, 134–144 (2018). CrossRef S. Sani Aliakbari, G. R. Ebrahimi, H. Vafaeenezhad, and A. R. Kiani-Rashid, “Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model,” J. Magnesium Alloys 6, 134–144 (2018). CrossRef
10.
go back to reference H. R. Rezaei Ashtiani and P. Shahsavari, “A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy,” J. Alloys Compd. 687, 263–273 (2016). CrossRef H. R. Rezaei Ashtiani and P. Shahsavari, “A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy,” J. Alloys Compd. 687, 263–273 (2016). CrossRef
11.
go back to reference H. Ahmadi, H. R. Rezaei Ashtiani, and M. Heidari, “A comparative study of phenomenological, physically-based and artificial neural network models to predict the hot flow behavior of API 5CT–L80 steel,” Mater. Today: Commun. 25, 101528 (2020). H. Ahmadi, H. R. Rezaei Ashtiani, and M. Heidari, “A comparative study of phenomenological, physically-based and artificial neural network models to predict the hot flow behavior of API 5CT–L80 steel,” Mater. Today: Commun. 25, 101528 (2020).
12.
go back to reference M. C. Dixit, N. Srivastava, and S. K. Rajput, “Modeling of flow stress of AA6061 under hot compression using artificial neural network,” Mater. Today: Proc. 4, 1964–1971 (2017). M. C. Dixit, N. Srivastava, and S. K. Rajput, “Modeling of flow stress of AA6061 under hot compression using artificial neural network,” Mater. Today: Proc. 4, 1964–1971 (2017).
13.
go back to reference Y. Huang, L. Liu, Z. Xiao, and S. Wang, “Hot deformation behavior of 6063 aluminum alloy studied using processing maps and microstructural analysis,” Phys. Met. Metallogr. 120, 1115–1125 (2019). CrossRef Y. Huang, L. Liu, Z. Xiao, and S. Wang, “Hot deformation behavior of 6063 aluminum alloy studied using processing maps and microstructural analysis,” Phys. Met. Metallogr. 120, 1115–1125 (2019). CrossRef
14.
go back to reference K. S. Pandya, C. C. Roth, and D. Mohr, “Strain rate and temperature dependent fracture of aluminum alloy 7075: experiments and neural network modeling,” Int. J. Plast. 135, 102788 (2020). CrossRef K. S. Pandya, C. C. Roth, and D. Mohr, “Strain rate and temperature dependent fracture of aluminum alloy 7075: experiments and neural network modeling,” Int. J. Plast. 135, 102788 (2020). CrossRef
15.
go back to reference C. Bruni, A. Forcellese, F. Gabrielli, and M. Simoncini, “Modeling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques,” J. Mater. Process. Technol. 177, 323–326 (2006). CrossRef C. Bruni, A. Forcellese, F. Gabrielli, and M. Simoncini, “Modeling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques,” J. Mater. Process. Technol. 177, 323–326 (2006). CrossRef
16.
go back to reference Y. V. R. K. Prasad and T. Seshacharyulu, “Modeling of hot deformation for microstructural control,” Int. Mater. Rev. 43, 243–258 (1998). CrossRef Y. V. R. K. Prasad and T. Seshacharyulu, “Modeling of hot deformation for microstructural control,” Int. Mater. Rev. 43, 243–258 (1998). CrossRef
17.
go back to reference H. He, Y. Yi, J. Cui, and S. Huang, “Hot deformation characteristics and processing parameter optimization of 2219 Al alloy using constitutive equation and processing map,” Vacuum 160, 293–302 (2019). CrossRef H. He, Y. Yi, J. Cui, and S. Huang, “Hot deformation characteristics and processing parameter optimization of 2219 Al alloy using constitutive equation and processing map,” Vacuum 160, 293–302 (2019). CrossRef
18.
go back to reference Y. C. Lin, L. T. Li, Y. C. Xia, and Y. Q. Jiang, “Hot deformation and processing map of a typical Al–Zn–Mg–Cu alloy,” J. Alloys Compd. 550, 438–445 (2013). CrossRef Y. C. Lin, L. T. Li, Y. C. Xia, and Y. Q. Jiang, “Hot deformation and processing map of a typical Al–Zn–Mg–Cu alloy,” J. Alloys Compd. 550, 438–445 (2013). CrossRef
19.
go back to reference Y. Sun, Z. Cao, Z. Wan, L. Hu, W. Ye, N. Li, et al., “3D processing map and hot deformation behavior of 6A02 aluminum alloy,” J. Alloys Compd. 742, 356–368 (2018). CrossRef Y. Sun, Z. Cao, Z. Wan, L. Hu, W. Ye, N. Li, et al., “3D processing map and hot deformation behavior of 6A02 aluminum alloy,” J. Alloys Compd. 742, 356–368 (2018). CrossRef
20.
go back to reference H. Matsumoto and V. Velay, “Mesoscale modeling of dynamic recrystallization behavior, grain size evolution, dislocation density, processing map characteristic, and room temperature strength of Ti–6Al–4V alloy forged in the (α+β) region,” J. Alloys Compd. 708, 404–413 (2017). CrossRef H. Matsumoto and V. Velay, “Mesoscale modeling of dynamic recrystallization behavior, grain size evolution, dislocation density, processing map characteristic, and room temperature strength of Ti–6Al–4V alloy forged in the (α+β) region,” J. Alloys Compd. 708, 404–413 (2017). CrossRef
21.
go back to reference H. Sun, Y. Zhang, A. A. Volinsky, B. Wang, B. Tian, K. Song, et al., “Effects of Ag addition on hot deformation behavior of Cu–Ni–Si alloys,” Adv. Eng. Mater. 19, 1600607 (2017). CrossRef H. Sun, Y. Zhang, A. A. Volinsky, B. Wang, B. Tian, K. Song, et al., “Effects of Ag addition on hot deformation behavior of Cu–Ni–Si alloys,” Adv. Eng. Mater. 19, 1600607 (2017). CrossRef
22.
go back to reference A. Rudra, S. Das, and R. Dasgupta, “Constitutive modeling for hot deformation behavior of Al-5083 + SiC composite,” J. Mater. Eng. Perform. 28, 87–99 (2019). CrossRef A. Rudra, S. Das, and R. Dasgupta, “Constitutive modeling for hot deformation behavior of Al-5083 + SiC composite,” J. Mater. Eng. Perform. 28, 87–99 (2019). CrossRef
23.
go back to reference D. Samantaray, S. Mandal, C. Phaniraj, and A. K. Bhaduri, “Flow behavior and microstructural evolution during hot deformation of AISI type 316 L(N) austenitic stainless steel,” Mater. Sci. Eng., A 528, 8565–8572 (2011). CrossRef D. Samantaray, S. Mandal, C. Phaniraj, and A. K. Bhaduri, “Flow behavior and microstructural evolution during hot deformation of AISI type 316 L(N) austenitic stainless steel,” Mater. Sci. Eng., A 528, 8565–8572 (2011). CrossRef
24.
go back to reference B. K. Barakhtin, E. A. Vasil’eva, Yu. M. Markova, K. A. Okhapkin, and S. N. Petrov, “Structural changes of a hot-deformed nickel alloy in mechanical energy dissipation processing maps,” Phys. Met. Metallogr. 120, 853–857 (2019). CrossRef B. K. Barakhtin, E. A. Vasil’eva, Yu. M. Markova, K. A. Okhapkin, and S. N. Petrov, “Structural changes of a hot-deformed nickel alloy in mechanical energy dissipation processing maps,” Phys. Met. Metallogr. 120, 853–857 (2019). CrossRef
25.
go back to reference M. S. Ozerdem and S. Kolukisa, “Artificial Neural Network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars,” J. Mater. Process. Technol. 199, 437–439 (2008). CrossRef M. S. Ozerdem and S. Kolukisa, “Artificial Neural Network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars,” J. Mater. Process. Technol. 199, 437–439 (2008). CrossRef
26.
go back to reference V. Senthilkumar, A. Balaji, and D. Arulkirubakaran, “Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite,” Trans. Nonferrous Met. Soc. China 23, 1737–1750 (2013). CrossRef V. Senthilkumar, A. Balaji, and D. Arulkirubakaran, “Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite,” Trans. Nonferrous Met. Soc. China 23, 1737–1750 (2013). CrossRef
27.
go back to reference Y. V. R. K. Prasad, “Processing maps: a status report,” J. Mater. Eng. Perform. 12, 638–645 (2003). CrossRef Y. V. R. K. Prasad, “Processing maps: a status report,” J. Mater. Eng. Perform. 12, 638–645 (2003). CrossRef
28.
go back to reference K. P. Rao and Y. V. R. K. Prasad, “Processing map and hot working mechanisms in a P/M TiAl alloy composite with in situ carbide and silicide dispersions,” Mater. Sci. Eng., A 527, 6589–6595 (2010). CrossRef K. P. Rao and Y. V. R. K. Prasad, “Processing map and hot working mechanisms in a P/M TiAl alloy composite with in situ carbide and silicide dispersions,” Mater. Sci. Eng., A 527, 6589–6595 (2010). CrossRef
29.
go back to reference S. Z. Zhu, T. J. Luo, T. A. Zhang, and Y. S. Yang, “Hot deformation behavior and processing maps of as-cast Mg–8Zn–1Al–0.5Cu–0.5Mn alloy,” Trans. Nonferrous Met. Soc. China 25, 3232–3239 (2015). CrossRef S. Z. Zhu, T. J. Luo, T. A. Zhang, and Y. S. Yang, “Hot deformation behavior and processing maps of as-cast Mg–8Zn–1Al–0.5Cu–0.5Mn alloy,” Trans. Nonferrous Met. Soc. China 25, 3232–3239 (2015). CrossRef
30.
go back to reference B. Li, Q. Pan, Z. Zhang, and C. Li, “Characterization of flow behavior and microstructural evolution of Al–Zn–Mg–Sc–Zr alloy using processing maps,” Mater. Sci. Eng., A 556, 844–848 (2012). CrossRef B. Li, Q. Pan, Z. Zhang, and C. Li, “Characterization of flow behavior and microstructural evolution of Al–Zn–Mg–Sc–Zr alloy using processing maps,” Mater. Sci. Eng., A 556, 844–848 (2012). CrossRef
31.
go back to reference H. R. Ezatpour, S. M. Haddad, S. A. Sajjadi, and Y. Huang, “Investigation of work softening mechanisms and texture in a hot deformed 6061 aluminum alloy at high temperature,” Mater. Sci. Eng., A 606, 240–247 (2014). CrossRef H. R. Ezatpour, S. M. Haddad, S. A. Sajjadi, and Y. Huang, “Investigation of work softening mechanisms and texture in a hot deformed 6061 aluminum alloy at high temperature,” Mater. Sci. Eng., A 606, 240–247 (2014). CrossRef
32.
go back to reference J. Yan, Q. L. Pan, B. Li, Z. Q. Huang, Z. M. Liu, and Z. M. Yin, “Research on the hot deformation behavior of Al–6.2Zn–0.70Mg–0.3Mn–0.17Zr alloy using processing map,” J. Alloys Compd. 632, 549–557 (2015). CrossRef J. Yan, Q. L. Pan, B. Li, Z. Q. Huang, Z. M. Liu, and Z. M. Yin, “Research on the hot deformation behavior of Al–6.2Zn–0.70Mg–0.3Mn–0.17Zr alloy using processing map,” J. Alloys Compd. 632, 549–557 (2015). CrossRef
33.
go back to reference M. H. Wang, L. Huang, M. L. Chen, and Y. L. Wang, “Processing map and hot working mechanisms of Cu–Ag alloy in hot compression process,” J. Cent. South Univ. 22, 821–828 (2015). CrossRef M. H. Wang, L. Huang, M. L. Chen, and Y. L. Wang, “Processing map and hot working mechanisms of Cu–Ag alloy in hot compression process,” J. Cent. South Univ. 22, 821–828 (2015). CrossRef
34.
go back to reference D. X. Wen, Y. C. Lin, J. Chen, J. Deng, X. M. Chen, J. L. Zhang, et al., “Effects of initial aging time on processing map and microstructures of a nickel-based superalloy,” Mater. Sci. Eng., A 620, 319–332 (2014). CrossRef D. X. Wen, Y. C. Lin, J. Chen, J. Deng, X. M. Chen, J. L. Zhang, et al., “Effects of initial aging time on processing map and microstructures of a nickel-based superalloy,” Mater. Sci. Eng., A 620, 319–332 (2014). CrossRef
35.
go back to reference H. R. Rezaei Ashtiani and P. Shahsavari, “Strain-dependent constitutive equations to predict high temperature flow behavior of AA2030 aluminum alloy,” Mech. Mater. 100, 209–218 (2016). H. R. Rezaei Ashtiani and P. Shahsavari, “Strain-dependent constitutive equations to predict high temperature flow behavior of AA2030 aluminum alloy,” Mech. Mater. 100, 209–218 (2016).
36.
go back to reference H. Zhang, R. Chen, X. D. Huang, and J. H. Chen, “Microstructural evolution of 2026 aluminum alloy during hot compression and subsequent heat treatment,” Trans. Nonferrous Met. Soc. China 21, 955–961 (2011). CrossRef H. Zhang, R. Chen, X. D. Huang, and J. H. Chen, “Microstructural evolution of 2026 aluminum alloy during hot compression and subsequent heat treatment,” Trans. Nonferrous Met. Soc. China 21, 955–961 (2011). CrossRef
37.
go back to reference X. M. Chen, Y. C. Lin, D. X. Wen, J. L. Zhang, and M. He, “Dynamic recrystallization behavior of a typical nickel-based superalloy during hot deformation,” Mater. Des. 57, 568–577 (2014). CrossRef X. M. Chen, Y. C. Lin, D. X. Wen, J. L. Zhang, and M. He, “Dynamic recrystallization behavior of a typical nickel-based superalloy during hot deformation,” Mater. Des. 57, 568–577 (2014). CrossRef
38.
go back to reference T. Sakai, A. Belyakov, R. Kaibyshev, H. Miura, and J. J. Jonas, “Dynamic and post-dynamic recrystallization under hot, cold and severe plastic deformation conditions,” Prog. Mater. Sci. 60, 130–207 (2014). CrossRef T. Sakai, A. Belyakov, R. Kaibyshev, H. Miura, and J. J. Jonas, “Dynamic and post-dynamic recrystallization under hot, cold and severe plastic deformation conditions,” Prog. Mater. Sci. 60, 130–207 (2014). CrossRef
39.
go back to reference Y. Zhang, Z. Chai, A. A. Volinsky, B. Tian, H. Sun, P. Liu, et al., “Processing maps for the Cu–Cr–Zr–Y alloy hot deformation behavior,” Mater. Sci. Eng., A 662, 320–329 (2016). CrossRef Y. Zhang, Z. Chai, A. A. Volinsky, B. Tian, H. Sun, P. Liu, et al., “Processing maps for the Cu–Cr–Zr–Y alloy hot deformation behavior,” Mater. Sci. Eng., A 662, 320–329 (2016). CrossRef
Metadata
Title
Processing Workability and Artificial Neural Network of AA1070 to the Prediction of Hot Flow Stress
Authors
H. R. Rezaei Ashtiani
A. A. Shayanpoor
Publication date
01-12-2021
Publisher
Pleiades Publishing
Published in
Physics of Metals and Metallography / Issue 13/2021
Print ISSN: 0031-918X
Electronic ISSN: 1555-6190
DOI
https://doi.org/10.1134/S0031918X21130159