Constitutive models to predict flow stress in Austenitic Stainless Steel 316 at elevated temperatures

Materials and Design

Volumn 43, Issue , 2013, Pages 410-418

  Gupta, Amit Kumar ^a   Anirudh, V K ^a   Singh, Swadesh Kumar ^b  

^a BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

^b GOKARAJU RANGARAJU INSTITUTE OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ARRHENIUS-TYPE EQUATION; ARTIFICIAL NEURAL NETWORK MODELS; COMPARATIVE STUDIES; CORRELATION COEFFICIENT; ELEVATED TEMPERATURE; JOHNSON-COOK MODEL; MATERIAL CONSTANT; SEMI-EMPIRICAL; SEMIEMPIRICAL MODELS; SIGNIFICANT IMPACTS; TESTING DATA; TRAINING DATA;

AUSTENITIC STAINLESS STEEL; CONSTITUTIVE MODELS; PLASTIC FLOW;

STRAIN RATE;

EID: 84864561106     PISSN: 02641275     EISSN: 18734197     Source Type: Journal    
DOI: 10.1016/j.matdes.2012.07.008     Document Type: Article

References (21)

1. Samantaray D., et al. A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified Austenitic Stainless Steel. Mater Sci Eng A 2009, 526:1-6.
2. Samantaray D., et al. A comparative study on Johnson Cook, modified Zerilli-Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr-1Mo steel. Comput Mater Sci 2009, 47(2):568-576.
3. Lin Y.-C., Chen M.-S., Zhang J. Modelling of flow stress of 42CrMo steel under hot compression. Mater Sci Eng A 2009, 499:88-92.
4. McQueen H.J., Ryan N.D. Constitutive analysis in hot working. Mater Sci Eng A 2002, 322:43-63.
5. 3 composite. Mater Sci Eng A 2001, 319-321:721-725.
6. Mandal Sumantra, Rakesh V., Sivaprasad P.V., Venugopal S., Kasiviswanathan K.V. Constitutive equations to predict high temperature flow stress in a Ti-modified Austenitic Stainless Steel. Mater Sci Eng A 2009, 500:114-121.
7. Zerilli F.J., Armstrong R.W. Dislocation mechanics based constitutive relations for material dynamics calculations. J Appl Phys 1987, 61:1816-1825.
8. Verleysen P., Peirs J., Slycken J.V., Faes K., Duchene L. Effect of strain rate on the forming behaviour of sheet metals. J Mater Proc Tech 2011, 211:1457-1464.
9. Rule William K., Jones S.E. A revised form for the Johnson-Cook strength model. Int J Impac Eng 1998, 21:609-624.
10. Khan A.S., Suh Y.S., Kazmi R. Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys. Int J Plasticity 2004, 20:2233-2248.
11. He Xiaoming., Zhongqi Yu., Lai Xinmin. A method to predict flow stress considering dynamic recrystallization during hot deformation. Comput Mater Sci 2008, 44:760-764.
12. Lin Y.C., Ge Liu A new mathematical model for predicting flow stress of typical high-strength alloy steel at elevated high temperature. Comput Mater Sci 2009, 48:54-58.
13. Phaniraj M.P., Lahiri A.K. The applicability of neural network model to predict flow stress for carbon steels. J Mater Proc Tech 2003, 141:219-227.
14. Srinivasulu S., Jain A. A comparative analysis of training methods for artificial neural network rainfall-runoff modelling. Appl Soft Comput 2006, 6:295-306.
15. Zener C., Hollomon H. Effect of strain rate upon plastic flow of steel. J Appl Phys 1944, 15:22.
16. Sellars C.M., McTegart W.J. On the mechanism of hot deformation. Acta Metall 1966, 14:1136-1138.
17. Xiao Y.-H., Guo C. Constitutive modelling for high temperature behavior of 1Cr12Ni3Mo2VNbN martensitic steel. Mater Sci Eng A 2011, 528:5081-5087.
18. Gupta A.K., Singh S.K., Reddy S., Hariharan G. Prediction of flow stress in dynamic strain aging regime of Austenitic Stainless Steel 316 using artificial neural network. J Mater Des 2012, 35:589-595.
19. Li P., Xue K.M., Lu Y., Tan J.R. Neural network prediction of flow stress of Ti-15-3 alloy under hot compression. J Mater Proc Tech 2004, 148:235-238.
20. Reddy N.S., Lee Y.H., Kim J.H., Lee C.S. High temperature deformation behavior of Ti-6Al-4V alloy with and equiaxed microstructure: a neural networks analysis. Met Mater Int 2008, 14(2):213-221.
21. Reddy N.S., Lee Y.H., Parka C.H., Lee C.S. Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed α+β microstructure by artificial neural networks. Mater Sci Eng A 2008, 492:276-282.