Prediction of mechanical properties of extra deep drawn steel in blue brittle region using Artificial Neural Network

Materials and Design

Volumn 31, Issue 5, 2010, Pages 2288-2295

  Singh, Swadesh Kumar ^a   Mahesh, K ^a   Gupta, Amit Kumar ^b  

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

^b BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

Artificial Neural Network; Blue brittle region; Extra deep drawn steel; Mechanical properties

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; BLUE BRITTLE REGION; BRITTLE REGION; DYNAMIC STRAIN AGEING; ELEVATED TEMPERATURE; EXTRA DEEP DRAWN STEEL; MATERIAL PROPERTY; MODELING ERRORS; PREDICTION OF MECHANICAL PROPERTIES; ROLLING DIRECTION; ROOM TEMPERATURE; STRAIN-HARDENING EXPONENT; ULTIMATE TENSILE STRENGTH; YIELD STRENGTH;

NEURAL NETWORKS; STRAIN; STRAIN HARDENING; TENSILE STRENGTH;

MECHANICAL PROPERTIES;

EID: 76549136781     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2009.12.012     Document Type: Article

References (15)

1. Klose F.B., and Ziegenbein A. Analysis of Portevin-Le Chatelier serrations of type B in Al-Mg. Mater Sci Eng A 369 (2004) 76-81
2. Pink E., and Kumar S. Serrated flow of aluminum alloys influenced by precipitates. Mater Sci Eng A 280 (2000) 17-24
3. Hall E.O. Yield point phenomenon in metals and alloys (1970), Macmillan, London
4. Peng K., Qian K., and Chen W. Effect of dynamic strain aging on high temperature properties of austenitic stainless steel. Mater Sci Eng A 379 (2004) 372-377
5. Brindley B.J. The effect of dynamic strain-ageing on the ductile fracture process in mild steel. Acta Metall 18 (1970) 325-329
6. Picu R.C., and Zhang D. Atomistic study of pipe diffusion in Al-Mg alloys. Acta Mater 52 (2004) 161-171
7. Sergio N.M., Iain L.M., and Luiz H.A. Effects of strain rate and dynamic strain aging on work hardening of an austenitic stainless steel. Scripta Metall 15 (1981) 581-584
8. Wilcox B.A., and Rosenfield A.R. On serrated yielding and negative strain-rate sensitivity. Chem Eng J 1 1 (1970) 83-84
9. *Al-Mg. Scripta Metall 7 2 (1973) 179-182
10. McCormick P.G. The Portevin-Le Chatelier effect in an Al-Mg-Si alloy. Acta Metall 19 5 (1971) 463-471
11. Charit I., Seok C.S., and Murty K.L. Synergistic effects of interstitial impurities and radiation defects on mechanical characteristics of ferritic steels. J Nucl Mater 361 (2007) 262-273
12. Wagner D., Moreno J.C., Prioul C., Frund J.M., and Houssin B. Influence of dynamic strain aging on the ductile tearing of C-Mn steels: modelling by a local approach method. J Nucl Mater 300 2-3 (2002) 178-191
13. Hastie T., Tibshirani R., and Friedman J. The elements of statistical learning: data mining inference and prediction (2001), Springer, New York
14. Mulford R.A., and Kocks U.F. New observations on the mechanisms of dynamic strain aging and of jerky flow. Acta Metall 27 (1979) 1125-1134
15. Sheikh H., and Serajzadeh S. Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect. J Mater Process Technol 196 (2008) 115-119