ORIGINAL_ARTICLE
Hot Deformation Behavior of 17-7 PH Stainless Steel
To investigate the hot deformation behavior of 17-7 PH stainless steel, hot compression tests were carried out at the temperatures of 950, 1050 and 1150 oC and strain rates of 0.001 s-1 to 0.1 s-1. Accordingly, the hot working behavior was studied by the analyses of flow stress curves, work hardening rate versus stress curves, exponent- type constitutive equations and deformed microstructures. Meanwhile, the average normalized critical stress for initiation of dynamic recrystallization (DRX) was determined using a 3rd order polynomial curve fitting. The results show that the flow stress depends strongly on the deformation temperature and the strain rate, and it increases with the deformation temperature decreasing and the strain rate increasing. Furthermore, it was found out that the co- existence of δ- ferrite lowers the softening rate at high Z (Zener- Holloman parameter) conditions. The experimental results were then used to determine the constants of constitutive equations. There is a good agreement between the measured and predicted results indicating a high accuracy of exponent- type constitutive equations.
http://ijmf.shirazu.ac.ir/article_3940_63fb5f0a629904e673235beb5a1e1553.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
1
11
10.22099/ijmf.2017.3940
: Hot deformation
Dynamic recrystallization
Constitutive analysis
17-7 PH stainless steel
M.
Zeinali
zeinali.mohammad.mse@gmail.com
true
1
Maleke Ashtar University of Technology
Maleke Ashtar University of Technology
Maleke Ashtar University of Technology
AUTHOR
E.
Shafiei
shafiei.ehsan.mse@gmail.com
true
2
Amirkabir University of Technology
Amirkabir University of Technology
Amirkabir University of Technology
LEAD_AUTHOR
K.
Farmanesh
e.shafiei@aut.ac.ir
true
3
Maleke Ashtar University of Technology
Maleke Ashtar University of Technology
Maleke Ashtar University of Technology
AUTHOR
R.
Hosseini
e.shafiei1@aut.ac.ir
true
4
Shiraz University
Shiraz University
Shiraz University
AUTHOR
R.
Sooltanipor
e.shafiei2@aut.ac.ir
true
5
Maleke Ashtar University of Tech.
Maleke Ashtar University of Tech.
Maleke Ashtar University of Tech.
AUTHOR
[1] H. Mirzadeh, M. H. Parsa and D. Ohadi, Hot deformation behavior of austenitic stainless steel for a wide range of initial grain size, Mater. Sci. Eng., A, 569 (2013) 54-60.
1
[2] L. L. Wang, R. B. Li, Y. G. Liao and M. Jin, Study on characterization of hot deformation of 403 steel, Mater. Sci. Eng., A, 567 (2013) 84-88.
2
[3] M. A. Mostafaei and M. Kazeminezhad, Hot deformation behavior of hot extruded Al–6Mg alloy, Mater. Sci. Eng., A, 535 (2012) 216-221.
3
[4] H. Mirzadeh, J. M. Cabrera and A. Najafizadeh, Constitutive relationships for hot deformation of austenite, Acta Mater., 59 (2011) 6441-6448.
4
[5] E. Shafiei and K. Dehghani, Prediction of single-peak flow stress curves at high temperatures using a new logarithmic-power function, J. Mater. Eng. Perform., 25(2016)4024-4035.
5
[6] M. Marchattivar, A. Sarkar, J. K. Chakravarty and B. P. Kashyap, Dynamic recrystallization during hot deformation of 304 austenitic stainless steel, J. Mater. Eng. Per., 22 (2013) 2168-2175.
6
[7] C. M. Cepeda- Jimenez, O. A. Ruano, M. Carsi and F. Cerreno, Study of hot deformation of an Al–Cu–Mg alloy using processing maps and microstructural characterization, Mater. Sci. Eng., A, 552 (2012) 530-539.
7
[8] A. Najafizadeh and J. J. Jonas, Predicting the critical stress for initiation of dynamic recrystallization, ISIJ Int., 46 (2006) 1679-1684.
8
[9] E. Shafiei and R. Ebrahimi, A modified model to estimate single peak flow stress curves of Ti-IF Steel, ISIJ Int., 52 (2012) 569-573.
9
[10] Y. Han, G. Liu, D. Zou, R. Liu and G. Qiao, Deformation behavior and microstructural evolution of as-cast 904L austenitic stainless steel during hot compression, Mater. Sci. Eng., A, 565 (2013) 342-350.
10
[11] A. Dehghan- Manshadi and P. D. Hadgson, Effect of δ-ferrite co-existence on hot deformation. and recrystallization of austenite, J. Mater. Sci., 43 (2003) 6272-6277.
11
[12] H. J. McQueen and N. D. Ryan, Constitutive analysis in hot working, Mater. Sci. Eng., A, 322 (2002) 43-47.
12
[13] E. Shafiei and R. Ebrahimi, A new constitutive equation to predict single peak flow stress curves, J. Eng. Mater. Tech., 135 (2013) 011006- 4.
13
[14] H. Mirzadeh and A. Najafizadeh, The rate of dynamic recrystallization in 17-4 PH stainless steel, Mater. Des.,31 (2010) 4577- 4583.
14
[15] E. I. Poliak and J. J. Jonas, Critical strain for dynamic recrystallization in variable strain rate, ISIJ Int., 43 (2003) 692- 700.
15
[16] H. Mirzadeh and A. Najafizadeh, Prediction of the critical conditions for initiation of dynamic recrystallization, Mater. Des.,31 (2010) 1174- 1179.
16
[17] A. Etaadi and K. Dehghani, Mater. Chem. Phy., A study on hot deformation behavior of Ni-42.5 Ti-7.5 Cu alloy, 140 (2013) 208- 215.
17
[18] H. Y. Wu, J. C. Yang, F. J. Zhu and C. T. Wu, Hot compressive flow stress modeling of homogenized AZ61 Mg alloy using strain-dependent constitutive equation, Mater. Sci. Eng., A,574 (2013)1724- 1726.
18
ORIGINAL_ARTICLE
A Robust RBF-ANN Model to Predict the Hot Deformation Flow Curves of API X65 Pipeline Steel
Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial function of strain power m (m is a constant). Root mean square error (RMSE) criterion was used assess the prediction performance of the investigated models. According to the results obtained, it was shown that the RBF-ANN model has a better performance than that of the investigated phenomenological model. Very low RMSE value of 0.41 MPa was obtained for RBF-ANN model that shows the robustness of it to predict the hot deformation flow curves of tested steel. The results can be further used in mathematical simulation of hot metal forming processes.
http://ijmf.shirazu.ac.ir/article_3941_585350b0769e04994985f543be5ce5a0.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
12
20
10.22099/ijmf.2017.3941
Hot deformation
Neural Computing
Radial Basis Function
Constitutive equations
Flow stress
M.
Rakhshkhorshid
m_rakhshkhorshid@yahoo.com
true
1
Department of Mechanical Engineering, Birjand University of Technology, POBOX 97175-569, Birjand, Iran
Department of Mechanical Engineering, Birjand University of Technology, POBOX 97175-569, Birjand, Iran
Department of Mechanical Engineering, Birjand University of Technology, POBOX 97175-569, Birjand, Iran
LEAD_AUTHOR
[1] G.R. Johnson and W.H. Cook, "A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. In: Proceedings of the 7th international symposium on ballistics, (1983) 541–543.
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[2] E. Voce, The relationship between stress and strain for homogeneous deformation. J. Inst. Met., 74 (1948) 537–562.
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[3] A.S. Khan and S. Huang, Experimental and theoretical study of mechanical behavior of 1100 aluminum in the strain rate range 10−5− 104 s−1, Int. J. Plast., 8 (1992) 397–424.
3
[4] H. Mirzadeh and A. Najafizadeh, Flow stress prediction at hot working conditions, Mater. Sci. Eng. A, 527(2010) 1160–1164.
4
[5] 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 (2011) 1733–1759.
5
[6] H. Shi, A.J. McLaren, C.M. Sellars, R. Shahani and R. Bolingbroke, Constitutive equations for high temperature flow stress of aluminium alloys, J. Mater. Sci. Technol., 13 (1997) 210-216.
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[7] Y.C. Lin, M.S. Chen and J. Zhang, Constitutive modeling for elevated temperature flow behavior of 42CrMo steel, Comput Mater Sci, 424 (2008) 470–477.
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[8] M.Y. Zhan, Z. Chen, H. Zhang and W. Xia, Flow stress behavior of porous FVS0812 aluminum alloy during hot-compression, Mech. Res. Commun., 33 (2006) 508–514.
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[9] P.J. Zerilli and R.W. Armstrong, Dislocation-mechanics-based constitutive relations for material dynamics calculations, J. Appl. Phys., 61 (1987) 1816-1825.
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[10] G.Z. Voyiadjis and A.H. Almasri, A physically based constitutive model for FCC metals with applications to dynamic hardness, Mech. Mater., 40 (2008) 549-563.
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[11] Y.C. Lin, M.S. Chen and J. Zhang, Prediction of 42CrMo steel flow stress at high temperature and strain rate, Mech. Res.Commun., 35 (2008) 142–50.
11
[12] Y.C. Lin, X.M. Chen, D.X. Wen and M.S. Chen, A physically-based constitutive model for a typical nickel-based superalloy, Comput. Mater. Sci., 83(2014) 282–289.
12
[13] M. Rakhshkhorshid and S.A. TeimouriSendesi, Bayesian Regularization Neural Networks for Prediction of Austenite Formation Temperatures (Ac1 and Ac3), J. Iron Steel Res. Int., 21(2) (2014) 246 – 251.
13
[14] V. Senthilkumar and A. Balaji, 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 (2013) 1737-1750.
14
[15] Y. Zhu, W. Zeng, Y. Sun, F. Feng and Y. Zhou, Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy, Comp. Mater. Sci., 50 (2011) 1785– 1790.
15
[16] H. Mirzadeh, J.M. Cabrera, J.M. Prado and A. Najafizadeh, Modeling and prediction of hot deformation flow curves, Metall. Mater. Trans. A, 43 (2012) 108–123.
16
[17] N. Haghdadi, A. Zarei-Hanzaki, A.R. Khalesian and H.R. Abedi, Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy, Mater. Des., 49 (2013) 386-391.
17
[18] Y.C. Lin, X. Fang and Y.P. Wang, Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network, Mater. Sci., 43 (2008) 5508-5515.
18
[19] N.S. Reddy, Y.H. Lee, C.H. Park and C.S. Lee, Prediction of flow stress in Ti–6Al–4V alloy with an equiaxed [alpha]+[beta] microstructure by artificial neural networks, Mater. Sci. Eng.A, 492 (2008) 276- 282.
19
[20] H.Y. Li, D.D. Wei, Y.H. Li and X.F. Wang, Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel, Mater. Des., 35 (2012) 557-562.
20
[21] S. Toros, F. Ozturk, Flow curve prediction of Al–Mg alloys under warm forming conditions at various strain rates by ANN, Appl. Soft. Comput., 110 (2011) 1891–1898.
21
[22] S. Mandal, P.V. Sivaprasad, S. Venugopal and K.P.N. Murthy, Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion, Appl. Soft. Comput., 9 (2009) 237–244.
22
[23] M. Rakhshkhorshid, Modeling the hot deformation flow curves of API X65 pipeline steel, Int. J. Adv. Manuf. Tech., 77 (2015) 203-210.
23
[24] M. Rakhshkhorshid and A.R. Maldar, A comparative study on constitutive modeling of hot deformation flow curves in AZ91 magnesium alloy, Iranian journal of materials Forming, 3(1) (2016) 27-37.
24
[25] M. Rakhshkhorshid and S.H. Hashemi, Experimental study of hot deformation behavior in API X65 steel, Mater. Sci. Eng. A, 573 (2013) 37–44.
25
[26] API Specifications 5L, Specifications for Line Pipe, 44th Edition, American Petroleum Institute, USA (2007).
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[27] M.S. Ozerdem and S. Kolukisa, Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys, Mater. Des., 30 (2009), 764–769.
27
[28] M. Zounemat-kermani, O. Kisi and T. Rajaee, Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed run off, Appl. Soft. Comput., 13 (2013) 4633– 4644.
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[29] S. Garg, S.K. Pal and D. Chakraborty, Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear, Neural Comput. &Applic., 16 (2007) pp. 407–417.
29
[30] .A. Mehrsai, H.R. Karimi, K.D. Thoben and B. Scholz-Reiter, Application of learning pallets for real- time scheduling by the use of radial basis function network, Neurocomputing, 101 (2013) 82–93.
30
[31] M. Rakhshkhorshid and S.H. Hashemi, Firefly algorithm assisted optimized NN to predict the elongation of API X65 pipeline steel, IJMMNO, 4(3) (2013), 238 – 251.
31
[32] MATLAB® software (2008) (Neural Network Toolbox, User's Guide)
32
[33] H. Sarnel and Y. Senol, Accurate and robust image registration based on radial basis neural networks, Neural Comput.&Applic., 20 (2011) 1255–1262.
33
ORIGINAL_ARTICLE
Nanotwins Formation in Accumulative Roll-Bonded Brass
Accumulative roll-bonding (ARB) is a severe plastic deformation process that is using rolling to produce ultrafine grains in coarse grained metallic materials. In this study, ARB has been applied on 70/30 brass up to 6 cycles at ambient temperature and non-lubricated conditions to apply a true strain up to 4.8 Von Mises strain. Microstructures of ARBed brass samples were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The results indicated that during ARB cycles, the grain size decreased from micron-size to nano-size and mechanical twins were widely observed throughout the microstructure after cycle 1. However after cycle 3, the twinning activity became significantly limited and deformation occurred via shear bands formation. After cycle 6, the measured average grain size was about 50 nm and nanotwins were observed originating from grain boundaries and gain boundary junctions. With the reduction in the grain size down to nanometer, the pole mechanism was not the dominant mechanism of nanotwin formation and nanotwins were mainly produced via partial dislocation emission from grain boundaries and grain boundary junctions.
http://ijmf.shirazu.ac.ir/article_3945_3ab69f83fa3104d341574b7daa126c22.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
21
27
10.22099/ijmf.2017.3945
Accumulative roll-bonding
nanotwins
nanostructured
70/30 brass
S.
Pasebani
somayeh.pasebani@oregonstate.edu
true
1
School of Mechanical, Industrial, and Manufacturing Engineering
Oregon State University
Corvallis, OR 97331-6001
USA
School of Mechanical, Industrial, and Manufacturing Engineering
Oregon State University
Corvallis, OR 97331-6001
USA
School of Mechanical, Industrial, and Manufacturing Engineering
Oregon State University
Corvallis, OR 97331-6001
USA
AUTHOR
M. R.
Toroghinejad
toroghi@cc.iut.ac.ir
true
2
Department of Materials Engineering, Isfahan University of Technology,
Isfahan
Department of Materials Engineering, Isfahan University of Technology,
Isfahan
Department of Materials Engineering, Isfahan University of Technology,
Isfahan
LEAD_AUTHOR
G.
Dini
g.dini@sci.ui.ac.ir
true
3
Isfahan University
Isfahan University
Isfahan University
AUTHOR
[1] Y. H. Zhao, X. Z. Liao, Z. Horita, T. G. Langdon and Y. T. Zhu, Mat. Sci. Eng. A. 493 (2008) 123-129.
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[3] C.X. Huang, K. Wang, S. D. Wu, Z. F. Zhang, G. Y. Li and S .X. Li, Acta. Mater. 54 (2006) 655-665.
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[4] V. Yamakov, D. Wolf, S. R. Phillpot, A. K. Mukherjee and H. Gleiter, Nature. Mater. 1(2002) 45-48.
4
[5] J. Schiotz and K. W. Jacobsen, Science 301 (2003) 1357-1359.
5
[6] H. V. Swygenhoven, Science 296 (2002) 66-67.
6
[7] N. Tsuji, Y. Saito, S. H. Lee and Y. Minamino, Adv. Eng. Mat. 5(2003) 338-344.
7
[8] W. Wang, Y. B. Wang, X. Z. Liao , Y. H. Zhao, E. J. Lavernia, Y. T. Zhu, Z. Horita and T. G Langdon, Scripta. Mater. 60 (2009) 52-55.
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[10] G.H. Xiao, N. R. Tao and K. Lu, Mat. Sci. Eng. A. 513-514 (2009) 13-21.
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[11] F.J. Humphreys and M. Hatherly, Recrystallization and related annealing phenomena, second ed, Elsevier Science Ltd, United Kingdom, 2004.
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[12] Z. Horita, D. J. Smith, M. Nemoto, R. Z. Valiev and T. G. Langdon, J. Mater. Res. 13(1998) 446-449.
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[13] X. Z. Liao, Y. H. Zhao, S. G. Srinivasan, Y. T. Zhu, R. Z. Valiev and D. V. Gunderov, Appl. Phys. Lett. 84(2004) 592-594.
13
[14] J. Cai, S. Shekhar, J. Wang and M. Ravi Shankar, Scripta Mater. 60(2009) 599-602.
14
[15] X. Z. Liao, Y. H. Zhao, Y. T. Zhu, R. Z. Valiev and D. V. Gunderov, J. Appl. Phys. 96(2004) 636-640.
15
[16] V. Yamakov, D. Wolf, S. R. Phillpot, A. K. Mukherjee and H. Gleiter, Nature. Mater. 3(2004) 43-47.
16
[17] H. V. Swygenhoven, M. Spacer and A. Caro, Acta. Mater. 47 (1999) 3117-3126.
17
[18] X. Z. Liao, F. Zhou, E. J. Lavernia, S. G. Srinivasan, M. I. Baskes, D. W. He and Y. T. Zhu, Appl. Phys. Lett. 83 (2003) 632- 634.
18
[19] X. Z. Liao, F. Zhou, E. J. Lavernia, D. W. He and Y.T. Zhu, Appl. Phys. Lett. 83 (2003) 5062-5064.
19
[20] X. Wu, Y. T. Zhu and E. Ma, Appl. Phys. Lett. 88 (2006) 121905- 121907.
20
[21] X. L. Wu and Y. T. Zhu, Appl. Phys. Lett. 89 (2006) 03192-03194.
21
[22] E. Ma, Y. M. Wang, Q. H. Lu, M. L. Sui, L. Lu and K. Lu, Appl. Phys. Lett. 85 (2004) 4932-4934.
22
[23] L. Lu, R. Schwaiger, Z. W. Shan, M. Dao, K. Lu and S. Suresh, Acta. Mater. 53 (2005) 2169- 2179.
23
ORIGINAL_ARTICLE
Modeling and Experimental Study of Static Recovery and Mechanical Behavior of AA5052 Alloy During Cold-Working and Subsequent Annealing
In the present study, the mechanical behavior of AA5052 aluminum alloy during cold deformation and subsequent isothermal annealing in a temperature range of 225-300oC was investigated using the uniaxial tensile test data. It is found that by increasing the annealing time and temperature the material yield strength is decreased. The microstructural investigations of the annealed samples show that the grains are elongated and there is no evidence of recrystallization. Hence, recovery is the main restoration phenomenon during the annealing treatment. The work hardening behavior of the alloy during cold work is modeled using a dislocation density based modeling approach and the softening behavior of deformed samples during subsequent annealing is modeled by applying a kinetics equation relating the yield strength to the annealing parameters. The kinetics equation is a nonlinear differential equation and it’s solved numerically by employing Runge-Kutta-Fehlberg (RKF) integration scheme which is coupled with Gauss-Newton nonlinear optimization technique to obtain the material constants of the model. The numerical results are validated using the experimental flow data.
http://ijmf.shirazu.ac.ir/article_3997_4b2074da2de127928a2cd4e32584bbcd.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
28
38
10.22099/ijmf.2017.3997
Kinetics of static recovery
AA5052 aluminum alloy
Cold working
Isothermal annealing
Nonlinear regression
M.
Seyed Salehi
majid.seyedsalehi@gmail.com
true
1
Department of materials science and engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of materials science and engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of materials science and engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
N.
Anjabin
anjabin@shirazu.ac.ir
true
2
shiraz university
shiraz university
shiraz university
LEAD_AUTHOR
[1] H. J. McQueen, S. Spigarelli, M. E. Kassner, and E. Evangelista, Hot deformation and processing of aluminum alloys: CRC Press (2011).
1
[2] A. Rollett, F. Humphreys, G. S. Rohrer, and M. Hatherly, Recrystallization and related annealing phenomena: Elsevier (2004).
2
[3] H. Mecking and U. Kocks, Kinetics of flow and strain-hardening, Acta Metallurgica, 29 (1981) 1865-1875.
3
[4] Y. Estrin and H. Mecking, A unified phenomenological description of work hardening and creep based on one-parameter models, Acta Metallurgica, 32 (1984) 57-70.
4
[5] G. Borelius, S. Berglund, and S. Sjoberg, Measurements on the Evolution of Heat During the Recovery of Cold-Worked Metals, Arkiv for Fysik, 6 (1953) 143-149.
5
[6] J. Friedel and R. Smoluchowski, Les dislocations, Physics Today, 10 (1957) 36.
6
[7] D. Kuhlmann, G. Masing, and J. Raffelsieper, On the theory of recovery, Zeitsch Metall, 40 (1949) 241-6.
7
[8] E. Nes, Recovery revisited, Acta metallurgica et materialia, 43 (1995) 2189-2207.
8
[9] M. Verdier, Y. Brechet, and P. Guyot, Recovery of AlMg alloys: flow stress and strain-hardening properties, Acta materialia, 47 (1998) 127-134.
9
[10] A. Standard, E8m-09: Standard Test Methods for Tension Testing of Metallic Materials, Annual Book of ASTM Standards, ASTM, West Conshohocken, PA, (2009) 127.
10
[11] J. Liu and J. G. Morris, Recrystallization microstructures and textures in AA 5052 continuous cast and direct chill cast aluminum alloy, Materials Science and Engineering: A, 385 (2004) 342-351.
11
[12] U. Kocks, Laws for work-hardening and low-temperature creep, Journal of engineering materials and technology, 98 (1976) 76-85.
12
[13] R. L. Burden and J. D. Faires, Numerical Analysis.(2001) Brooks/Cole, USA.
13
[14] W. Poole, M. Militzer, and M. Wells, Modelling recovery and recrystallisation during annealing of AA 5754 aluminium alloy, Materials science and technology, 19 (2003) 1361-1368.
14
[15] A. Seeger, D. Wolf, and H. Mehrer, Analysis of tracer and nuclear magnetic resonance measurements of self‐diffusion in aluminium, physica status solidi (b), 48 (1971) 481-496.
15
[16] M. Mantina, Y. Wang, L. Chen, Z. Liu, and C. Wolverton, First principles impurity diffusion coefficients, Acta Materialia, 57 (2009) 4102-4108.
16
[17] W. A. Soer, Interactions between dislocations and grain boundaries, (2006).
17
[18] L. F. Mondolfo, Structure and properties of aluminum alloys, Metallurgiya, Moscow, (1979).
18
ORIGINAL_ARTICLE
A Comparative Study on the Formability Prediction of Two-Layer Metallic Sheets
Two-layer metallic sheets have wide applications in aerospace, marine, automotive and domestic industries due to their superlative characteristics. In this paper, the formability of two-layer sheet is investigated through analytical, experimental and numerical approaches. An analytical model is developed based on Marciniak-Kuczynski method associated Hill’s non-quadratic yield criterion. Forming limit diagrams are also obtained numerically based on finite element method using Bifurcation theory and ductile fracture criteria. Furthermore, experiments are carried out on Al3105-St14 two-layer sheet. Theoretical results from various methods are compared with results obtained from experiments to evaluate the competency of discussed analytical and numerical methods to predict the formability of two-layer sheets. The results show that analytical and numerical approaches discussed in this paper have good capabilities to predict the formability of two-layer sheets. However, the analytical method based on M-K model and numerical approach based on bifurcation theory are more suitable to determine the forming limit diagram of Al3105-St14 two-layer sheets.
http://ijmf.shirazu.ac.ir/article_3998_60d2d746cb3135c265c7a57335de879f.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
39
51
10.22099/ijmf.2017.3998
Forming Limit Diagram
Two-layer Sheet
Marciniak-Kuczynski (M-K) Method
Bifurcation Theory
Ductile Fracture
H.
Deilami Azodi
hdazodi@arakut.ac.ir
true
1
Arak University of Technology
Arak University of Technology
Arak University of Technology
LEAD_AUTHOR
R.
Darabi
royadrb8989@yahoo.com
true
2
Arak University of Technology
Arak University of Technology
Arak University of Technology
AUTHOR
[1] S. P. Keeler, Circular grid system – a valuable aid for evaluating sheet metal formability, SAE Technical Paper, 77 (1968) 371–379.
1
[2] G. M. Goodwin, Application of strain analysis to sheet metal forming problems in press shop, SAE Transactions, 77 (1968) 380–387.
2
[3] S. L. Semiatin, H. R. Piehler, Formability of sandwich sheet materials in plane strain compression and rolling, Metallurgical Transactions A, 10 (1979), 97–107.
3
[4] S. L. Semiatin, H. R. Piehler, Deformation of sandwich sheet materials in uniaxial tension, Metallurgical Transactions A, 10 (1979) 85–96.
4
[5] T. Mori, S. Kurimoto, Press-formability of stainless steel and aluminum clad sheet, Journal of Materials Processing Technology, 56 (1996) 242–253.
5
[6] F. Yoshida, R. Hino, Forming limit of stainless steel-clad aluminum sheets under plane stress condition, Journal of Materials Processing Technology, 63 (1997) 66–71.
6
[7] -A. Jalali Aghchai, M. Shakeri, B. Mollaei Dariani, Theoretical and experimental formability study of two-layer metallic sheet Al1100/St12, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering manufacture, 222 (9) (2008) 1131–1138.
7
[8] -A.Jalali Aghchai, M. Shakeri, B. Mollaei Dariani, Influences of material properties of components on formability of two-layer metallic sheets, International Journal of Advanced Manufacturing Technoloogy, 66 (2012) 809–823.
8
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22
ORIGINAL_ARTICLE
Prediction of Extrusion Pressure in Vortex Extrusion Using a Streamline Approach
Vortex extrusion (VE) is a severe plastic deformation technique which is based on the synergies between high strain accumulation and high hydrostatic pressure. Such a high amount of pressure, places a mandate to seek the method for investigation of the load under processing conditions. For this, kinematically admissible velocity field and upper bound terms based on Bezier formulation are developed in order to investigate relative pressure in the VE process. Effects of reduction in area, relative length, twist angle, and friction factor in power dissipation terms are systematically analyzed. It is demonstrated that increasing the twist angle and area reducing and friction factor in the VE process increases the relative pressure, which the rates of these increase varies with twist angle. Moreover, the effect of the relative length is different in various frictional conditions. Results of conventional extrusion (CE) are in good agreement with those found by Avitzur for the effect of slug length and friction factor on the relative extrusion stress.
http://ijmf.shirazu.ac.ir/article_4060_5bd7ff1ef9abc19fe52fb057609f8630.pdf
2017-04-01T11:23:20
2018-02-25T11:23:20
52
62
10.22099/ijmf.2017.4060
Severe plastic deformation
Vortex extrusion
Bezier formulation
Upper bound theorem
M.
Shahbaz
mehredads1@gmail.com
true
1
-Department of Materials Science and Engineering, School of Engineering, Urmia University, Urmia, Iran
-Department of Materials Science and Engineering, School of Engineering, Urmia University, Urmia, Iran
-Department of Materials Science and Engineering, School of Engineering, Urmia University, Urmia, Iran
LEAD_AUTHOR
J. G.
Kim
junggi91@gmail.com
true
2
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
AUTHOR
R.
Ebrahimi
ebrahimy@shirazu.ac.ir
true
3
Department of Materials Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
Department of Materials Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
Department of Materials Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
AUTHOR
H. S.
Kim
hyoungseopkim@gmail.com
true
4
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
Department of Materials Science and Engineering, POSTECH, Pohang 790-784, Republic of Korea
AUTHOR
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[10] M.I. Latypov, M.G. Lee, Y. Beygelzimer, R. Kulagin and H.S. Kim, On the simple shear model of twist extrusion and its deviations, Metals and Materials International, 21 (2015) 569-579.
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21