Temperature-Dependent SRS Behavior of 316L and Its Constitutive Model

doi:10.1007/s40195-017-0697-x

Abstract

Abstract:

The strain rate sensitivity (SRS) and temperature sensitivity (TS) of 316L austenitic stainless steel were investigated by constant strain rate test (CSRT) and strain rate jump test (SRJT) under four temperatures (293, 373, 473 and 573 K) and four strain rates (5 × 10^-4/s, 1 × 10^-3/s, 5 × 10^-3/s and 1 × 10^-2/s). The results show that temperature sensitivity (TS) indexes at different strain rates are coincidence to be negative, related to temperature softening. On the contrary, SRS indexes change from positive to negative with the increase in temperature associated with dynamic strain aging (DSA). Moreover, based on the comparison between CSRT and SRJT, SRS and TS indexes obtained by two methods agree well. It proves that the SRJT can describe the SRS and TS phenomenon of 316L efficiently. Furthermore, the effects of temperature and strain rate on fracture mechanism were discussed. At last, an improved Johnson-Cook model was proposed to consider the temperature-dependent SRS behavior of 316L.

Key words: Strain rate sensitivity, Strain rate jump test, Improved, Johnson-Cook model

Jian Peng, Jian Peng, Kai-Shang Li, Jun-Feng Pei, Chang-Yu Zhou. Temperature-Dependent SRS Behavior of 316L and Its Constitutive Model[J]. Acta Metallurgica Sinica (English Letters), 2018, 31(3): 234-244.

Figures/Tables 15

Fig. 1 Comparison between the results of SRJT and CSRT: a the control mode of strain rate; b the entire view of stress-strain curves; c-f the local magnified views at different temperatures

Fig. 2 SRS indexes obtained by CSRT at different temperatures

Fig. 3 Back extrapolation method to calculate the SRS index: a the entire view; b the local enlarged section

Fig. 4 SRS indexes obtained by SRJT at different temperatures

Fig. 5 TS indexes at different strains and strain rates: a the results of CSRT (strain rate = 5 × 10-4/s); b the results of SRJT

Fig. 6 Comparison between the results of SRJT and CSRT: a SRS indexes; b TS indexes

Fig. 7 Fracture morphologies at different temperatures under the strain rate of 5 × 10-4/s: a, b 293 K; c, d 373 K; e, f 473 K; g, h 573 K

Fig. 8 Fracture morphologies at different strain rates under 573 K: a, b 5 × 10-4/s; c, d 1 × 10-2/s

Table 1 Characteristics of different constitutive models

	Model equation	Number of material constants
Arrhenius [28, 29]	\(\dot{\varepsilon}\)=A[sinh(ασ)]ⁿexp(-Q/RT)	20 (with 4th polynomial)
Johnson-Cook [11, 30]	σ=(A+Bεⁿ)(1+Cln\(\dot{\varepsilon}\)^?)(1-T^?m)	5
Zerilli-Armstrong [31, 32]	σ=(C₁+C₂εⁿ)exp{(C₃+C₄ε)T^?+(C₅+C₆T^?)ln\(\dot{\varepsilon}\)^?}	6
Neural network [33, 34]	Artificial intelligence technology	-

Fig. 9 Calculating procedure of material parameters: a, b material parameters in the classical model; c variation of C with the relative temperature

Table 2 Parameters of classical J-C model

A (MPa)	B (MPa)	n	C	m
356.09	1264.35	0.77	0.008	1.28

Table 3 Development of Johnson-Cook model

Researchers	Materials	Improvement
Lin et al. [16] Li et al. [17] He et al. [36]	Alloy steel	Considering the coupled effects of strain rate and temperature
Zhao et al. [37]	Fe-Cr alloy	Considering strain rate softening
Wang et al. [38]	Inconel 718	Relating parameter C with strain rate and temperature by sine function

Table 4 Parameters of the improved J-C model

A (MPa)	B (MPa)	n	C ₁	C ₂	C ₃	D ₁	D ₂	D ₃
356.09	1264.35	0.77	0.008	0.006	0.08	0.996	- 1.14	1.83

Fig. 10 Comparison of predicted curves between classical J-C model and improved J-C model (take strain rate = 5 × 10-4/s as an example)

Fig. 11 Comparison of statistical parameters between classical J-C model and improved J-C model at different temperatures Since the improved J-C model suitably considers the temperature-dependent SRS behavior with Eq. (13), correlation coefficients and mean errors of the improved J-C model hardly vary with temperature. Above all, comparing statistical parameters of two constitutive models, the improved J-C model considering the temperature-dependent SRS behavior can describe the SRS and TS of 316L with higher accuracy.

References

[1]	Y.C. Lin, M. He, M. Zhou, D.X. Wen, J. Chen, J. Mater. Eng. Perform. 24, 3527(2015)
[2]	Y. Yan, W.P. Deng, Z.F. Gao, Acta Metall.. Sin.(Engl. Lett.) 29, 163(2013)
[3]	J. Peng, C.Y. Zhou, Q. Dai, X.H. He, Mater. Des. 50, 968(2013)
[4]	X.F. Li, J. Chen, L.Y. Ye, Acta Metall.. Sin.(Engl. Lett.) 26, 657(2013)
[5]	H.K. Yang, Y.Z. Tian, Z.J. Zhang, Z.F. Zhang, Mater. Sci. Eng. A 655,251 (2016)
[6]	P. Verma, S. Rao, P. Chellapandi, Mater. Sci. Eng. A 621,39 (2015)
[7]	X.D. Bian, F.P. Yuan, X.L. Wu, Mater. Sci. Eng. A 696,220 (2017)
[8]	R. Kasadaa, S. Konishia, D. Hamaguchi, Fusion. Eng. Des. 109, 1507(2016)
[9]	S.R. Bodner, Y. Partom, J. Appl. Mech. 42, 385(1975)
[10]	H.S. Chen, W.X. Wang, H.H. Nie, Y.L. Li, Q.C. Wu, P. Zhang, Acta Metall.. Sin.(Engl. Lett.) 28, 1214(2015)
[11]	G.R. Johnson, W.H. Cook, Eng. Fract. Mech. 21, 31(1985)
[12]	F.J. Zerilli, R.W. Armstrong, J. Appl. Phys. 61, 1816(1987)
[13]	Z.B. Huang, M. Wan, H. Wu, J. Plast. Eng. 20, 89(2013)
[14]	Y.C. Lin, X.M. Chen, Mater. Des. 32, 1733(2011)
[15]	Y.C. Lin, Q.F. Li, Y.C. Xia, Mater. Sci. Eng. A 534,654 (2012)
[16]	Y.C. Lin, X.M. Chen, G. Liu, Mater. Sci. Eng. A 527,6980 (2010)
[17]	H.Y. Li, Y.H. Li, X.F. Wang, Mater. Des. 49, 493(2013)
[18]	W.C. Jiang, J.M. Gong, S.T. Tu, Mater. Des. 31, 2157 (2010)
[19]	W.C. Jiang, B. Yang, X.W. Guan, Y. Luo, Acta Metall.. Sin.(Engl. Lett.) 26, 241(2013)
[20]	P. Ferro, A. Fabrizi, F. Bonollo, Acta Metall.. Sin.(Engl. Lett.) 29, 859(2016)
[21]	GB/T 228.1-2010
[22]	W.A. Backoken, I.R. Turner, D.H. Avery, J. Mater. Sci. 6, 1061(1971)
[23]	K.W. Qian, J. Fuzhou. Univ. 16, 57(1988)
[24]	E.I. Samuel, B.K. Choudhary, K.B.S. Scr. Mater. 46, 507(2002)
[25]	Y.Q. Song, Z.P. Guan, Z. Li, Sci. China (Ser. E) 37, 1363(2007)
[26]	Y.C. Lin, J. Deng, Y.Q. Jiang, Mater. Des. 55, 949(2014)
[27]	B.K. Choudhary, Metall. Mater. Trans. A 44,4979 (2013)
[28]	J. Yan, Q.L. Pan, A.D. Li, W.B. Song, Trans. Nonferrous Met. Soc. China 27,638 (2017)
[29]	Y.H. Zhang, W. Zhang, J.L. Gao, Z.M. Yuan, W.G. Bu, Acta Metall.. Sin.(Engl. Lett.) 30, 1040(2017)
[30]	F. Ducobu, E. Rivière-Lorphèvre, E. Filippi, Int. J. Mech. Sci. 122, 143(2017)
[31]	T. Mirzaiea, H. Mirzadeha, J.M. Cabrera, Mech. Mater. 94, 38(2016)
[32]	D.Y. Li, Z.W. Zhu, S.N. Xiao, G.H. Zhang, Y.S. Lu, Mater. Sci. Eng. A 707,459 (2017)
[33]	D.D. Chen, Y.C. Lin, Y. Zhou, M.S. Chen, D.X. Wen, J. Alloys Compd. 708, 938(2017)
[34]	Y.C. Lin, Y.J. Liang, M.S. Chen, X.M. Chen, Appl. Phys. A 123,68 (2017)
[35]	L. Gambirasio, E. Rizzi, Comput. Mater. Sci. 113, 231(2016)
[36]	A. He, G.L. Xie, H.L. Zhang, X.T. Wang, Mater. Des. 52, 677(2013)
[37]	Y.H. Zhao, J. Sun, J.F. Li, Y.Q. Yan, P. Wang, J. Alloys Compd. 723, 179(2017)
[38]	X.Y. Wang, C.Z. Huang, B. Zou, H.L. Liu, H.T. Zhu, J. Wang, Mater. Sci. Eng. A 580,385 (2013)