Abstract
In this research, the hot processing parameters-impact toughness correlation of Ti-6Al-4V titanium alloy is studied. Fifty-four groups of hot processing treatments with different forging temperatures (930, 950, 970 °C), deformation degrees (20, 50, 80%), annealing temperatures (600, 700, 800 °C), and annealing time (1 and 5 h) were conducted. The orthogonal design was used to find the primary hot processing parameters influencing the impact toughness of Ti-6Al-4V alloy. The results show that the annealing temperature can exert the biggest influence on impact toughness. Low annealing temperature is essential to achieve high impact toughness value. In addition, the BP neural network was used to describe the quantitative correlation between hot processing parameters and impact toughness. The results show that the BP neural network exhibits good performance in predicting the impact toughness of Ti-6Al-4V alloy. The prediction error is within 5%. The BP neural network and the orthogonal design method are mutually confirmed in the present work. Finally, based on the microstructure analysis, the reasons responsible for above experimental results are explained.
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References
B.K. Singh and V. Singh, Effect of Fast Neutron Irradiation on Tensile Properties of AISI, 304 Stainless Steel and Alloy Ti-6Al-4V, Mater. Sci. Eng. A, 2011, 528(16-17), p 5336–5340
Z.H. Huang, W.J. Qi, and J. Xu, Effect of Microstructure on Impact Toughness of Magnesium Alloys, Trans. Nonferrous Met. Soc. China, 2012, 22(10), p 2334–2342
T.J. Ma, W.Y. Li, and S.Y. Yang, Impact Toughness and Fracture Analysis of Linear Friction Welded Ti-6Al-4V Alloy Joints, Mater. Des., 2009, 30(6), p 2128–2132
S.L. Semiatin, V. Seetharaman, and I. Weiss, Flow Behavior and Globularization Kinetics during Hot Working of Ti-6Al-4V with a Colony Alpha Microstructure, Mater. Sci. Eng. A, 1999, 263(2), p 257–271
T. Seshacharyulu, S.C. Medeiros, W.G. Frazier, and Y.V.R.K. Prasad, Microstructural Mechanisms During Hot Working of Commercial Grade Ti-6Al-4V with Lamellar Starting Structure, Mater. Sci. Eng. A, 2002, 325(1-2), p 112–125
S.L. Semiatin and T.R. Bieler, The Effect of Alpha Platelet Thickness on Plastic Flow during Hot Working of Ti-6Al-4V with a Transformed Microstructure, Acta Mater., 2001, 49(17), p 3565–3573
R.K. Nalla, I. Altenberger, U. Noster, G.Y. Liu, B. Scholtes, and R.O. Ritchie, On the Influence of Mechanical Surface Treatments-Deep Rolling and Laser Shock Peening: On the Fatigue Behavior of Ti-6Al-4V at Ambient and Elevated Temperatures, Mater. Sci. Eng. A, 2003, 355(1-2), p 216–230
R. Ding, Z.X. Guo, and A. Wilson, Microstructural Evolution of a Ti-6Al-4V Alloy During Thermomechanical Processing, Mater. Sci. Eng. A, 2002, 327(2), p 233–245
H. Güleryüz and H. Çimenoğlu, Effect of Thermal Oxidation on Corrosion and Corrosion: Wear Behaviour of a Ti-6Al-4V Alloy, Biomaterials, 2004, 25(16), p 3325–3333
G. Thomas, V. Ramachandra, R. Ganeshan, and R. Vasudevan, Effect of Pre- and Post-Weld Heat Treatments on the Mechanical Properties of Electron Beam Welded Ti-6Al-4V Alloy, J. Mater. Sci., 1993, 28(18), p 4892–4899
M.W. Wu, P.H. Lai, and J.K. Chen, Anisotropy in the Impact Toughness of Selective Laser Melted Ti-6Al-4V Alloy, Mater. Sci. Eng. A, 2016, 650, p 295–299
M. Balasubramanian, V. Jayabalan, and V. Balasubramanian, A Mathematical Model to Predict Impact Toughness of Pulsed-Current Gas Tungsten Arc-Welded Titanium Alloy, Int. J. Adv. Manuf. Technol., 2008, 35, p 852–858
H.H. Yu, R. Xing, S. Liu, C.P. Li, Z.Y. Guo, and P.C. Li, Studies on the Hemolytic Activity of Tentacle Extracts of Jellyfish Rhopilema esculentum Kishinouye: Application of Orthogonal Test, Int. J. Biol. Macromol., 2007, 40(3), p 276–280
Y. Bai, H.M. Gao, L. Wu, Z.H. Ma, and N. Cao, Influence of Plasma-MIG Welding Parameters on Aluminum Weld Porosity by Orthogonal Test, Trans. Nonferrous Met. Soc. China, 2010, 20(8), p 1392–1396
Y. Sun, W.D. Zeng, Y.F. Han, X. Ma, Y.Q. Zhao, P. Guo, G. Wang, and M.S. Dargusch, Determination of the Influence of Processing Parameters on the Mechanical Properties of the Ti-6Al-4V Alloy Using an Artificial Neural Network, Comput. Mater. Sci., 2012, 60, p 239–244
A.F. Yetim, M.Y. Codur, and M. Yazici, Using of Artificial Neural Network for the Prediction of Tribological Properties of Plasma Nitrided 316L Stainless Steel, Mater. Lett., 2015, 158, p 170–173
A. Powar and P. Date, Modeling of Microstructure and Mechanical Properties of Heat Treated Components by Using Artificial Neural Network, Mater. Sci. Eng. A, 2015, 628, p 89–97
N.S. Reddy, B.B. Panigrahi, C.M. Ho, J.H. Kim, and C.S. Lee, Artificial Neural Network Modeling on the Relative Importance of Alloying Elements and Heat Treatment Temperature to the Stability of α and β Phase in Titanium Alloys, Comput. Mater. Sci., 2015, 107, p 175–183
M. Çöl, H.M. Ertunc, and M. Yılmaz, An Artificial Neural Network Model for Toughness Properties in Microalloyed Steel in Consideration of Industrial Production Conditions, Mater. Des., 2007, 28(2), p 488–495
Z.C. Sun, H. Yang, and Z. Tang, Microstructural Evolution Model of TA15 Titanium Alloy Based on BP Neural Network Method and Application in Isothermal Deformation, Comput. Mater. Sci., 2010, 50(2), p 308–318
Z.C. Sun, X.Q. Wang, J. Zhang, and H. Yang, Prediction and Control of Equiaxed α in Near-β Forging of TA15 Ti-alloy Based on BP Neural Network: For Purpose of Tri-modal Microstructure, Mater. Sci. Eng. A, 2014, 591, p 18–25
S.P. Kosbatwar and S.K. Pathan, Pattern Association for Character Recognition by Back-Propagation Algorithm Using Neural Network Approach, Int. J. Adv. Stud. Comput. Sci. Eng., 2012, 3, p 127–134
B. Chen, X.R. Cheng, Y.S. Hu, and Y. Ren, Application of Back-Propagation Neural Network for Controlling the Front End Bending Phenomenon in Plate Rolling, Int. J. Mater. Prod. Technol., 2013, 46(2-3), p 166–176
Y. Sun, W.D. Zeng, Y.F. Han, Y.Q. Zhao, G. Wang, M.S. Dargusch, and P. Guo, Modeling the Correlation between Microstructure and the Properties of the Ti-6Al-4V Alloy Based on an Artificial Neural Network, Mater. Sci. Eng. A, 2011, 528(29-30), p 8757–8764
M.T. Hagan, H.B. Demuth, and M.H. Beale, Neural Network Design, Thomson Learning, Singapore, 2002
Y.G. Zhou, W.D. Zeng, and H.Q. Yu, An Investigation of a New Near-Beta Forging Process for Titanium Alloys and Its Application in Aviation Components, Mater. Sci. Eng. A, 2005, 393, p 204–212
Acknowledgment
This work was financially supported by Research Fund for the Doctoral Program of Higher Education of China with No. 20116102110015, the New Century Excellent Talents in University with No. NCET-07-0696, and the National 973 Project of China with No. 2007CB613807.
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Shi, X., Zeng, W., Sun, Y. et al. Study on the Hot Processing Parameters-Impact Toughness Correlation of Ti-6Al-4V Alloy. J. of Materi Eng and Perform 25, 1741–1748 (2016). https://doi.org/10.1007/s11665-016-2050-3
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DOI: https://doi.org/10.1007/s11665-016-2050-3