Effect of different environmental parameters on pitting behavior of AISI type 316L stainless steel: Experimental studies and neural network modeling

Materials and Design

Volumn 30, Issue 9, 2009, Pages 3770-3775

  Ramana, K V S ^a,b   Anita, T ^c   Mandal, Sumantra ^c   Kaliappan, S ^b   Shaikh, H ^c   Sivaprasad, P V ^c   Dayal, R K ^c   Khatak, H S ^c  

^a R M D ENGINEERING COLLEGE   (India)

^b ANNA UNIVERSITY   (India)

^c INDIRA GANDHI CENTRE FOR ATOMIC RESEARCH   (India)

Author keywords

Artificial neural network; Austenitic stainless steel; Pitting corrosion

Indexed keywords

AQUEOUS ENVIRONMENT; ARTIFICIAL NEURAL NETWORK; CHLORIDE CONCENTRATIONS; CHLORIDE ION CONCENTRATION; ELECTROCHEMICAL POLARIZATION TEST; ENVIRONMENTAL PARAMETER; EXPERIMENTAL STUDIES; GOOD CORRELATIONS; INTER-RELATIONSHIPS; NEURAL NETWORK MODELING; PITTING BEHAVIOR; PITTING CORROSION; PITTING POTENTIAL; POLARIZATION TESTS; TYPE 316L STAINLESS STEEL;

AUSTENITE; AUSTENITIC STAINLESS STEEL; BACKPROPAGATION; CHLORINE COMPOUNDS; CORROSION RESISTANT ALLOYS; NEURAL NETWORKS; POLARIZATION; STEEL; STEEL METALLURGY; STEEL RESEARCH;

PITTING;

EID: 67349274369     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2009.01.039     Document Type: Article

References (30)

1. Shaikh H., George G., Schneider F., Mummert K., and Khatak H.S. Stress corrosion crack growth studies on nitrogen added AISI type 316 stainless steel and its weld metal in boiling acidified sodium chloride solution using the fracture mechanics approach. Werkst Korros 51 (2000) 719
2. Shaikh H., Sivaprasad P.V., Vinoy T.V., Bharathi G., Venkadesan S., and Khatak H.S. Fabrication strain rate-stress corrosion property correlations of thermomechanically treated 15Cr-15Ni-2.2Mo titanium modified austenitic stainless steel. Mater Sci Technol 14 (1998) 1175
3. Shaikh H., Sivaibharasi N., Sasi B., Anita T., Amirthalingam R., Rao B.P.C., et al. Use of eddy current testing method in detection and evaluation of sensitization and intergranular corrosion in austenitic stainless steels. Corros Sci 48 (2006) 1462
4. Trethewey R.K. Some observations on the current status in the understanding of stress-corrosion cracking of stainless steels. Mater Design 29 (2008) 501
5. Shaikh H., Khatak H.S., Dayal R.K., Gnanamoorthy J.B., and Sur A. Failure analysis of an AM 350 steel bellows. Practical Metall 28 (1991) 143
6. Vinoy T.V., Shaikh H., Khatak H.S., Gnanamoorthy J.B., and Raj B. Metallurgical failure analysis of a failed containment building door bellows of a nuclear reactor. Practical Metall 34 (1997) 527
7. Sedriks A.J. Effects of alloy composition and microstructure on the passivity of stainless steels. Corrosion 42 (1986) 376
8. Sha W., and Edwards K.L. The use of artificial neural networks in materials science based research. Mater Design 28 (2007) 1747
9. Yilmaz M., and Metin Ertunc H. The prediction of mechanical behavior for steel wires and cord materials using neural networks. Mater Design 28 (2007) 599
10. Guo Z., Malinov S., and Sha W. Modelling beta transus temperature of titanium alloys using artificial neural network. Comput Mater Sci 32 (2005) 1
11. Cottis R.A., Qing Li., Owen G., Gartland S.J., Helliwell I.A., and Turega M. Neural network methods for corrosion data reduction. Mater Design 20 (1999) 169
12. Smets H.M.G., and Bogaerts W.F.L. Deriving corrosion knowledge from case histories: the neural network approach. Mater Design 13 (1992) 149
13. Malinov S., and Sha W. Application of artificial neural networks for modeling correlations in titanium alloys. Mater Sci Eng A 365 (2004) 202
14. Parthiban T., Ravi R., Parthiban G.T., Srinivasan S., Ramakrishnan K.R., and Raghavan M. Neural network analysis for corrosion of steel in concrete. Corros Sci 47 (2005) 625
15. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Prentice Hall, Upper Saddle River, New Jersey
16. Mandal S., Sivaprasad P.V., Venugopal S., and Murthy K.P.N. Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict. Modell Simul Mater Sci Eng 14 (2006) 1053
17. Mandal S., Sivaprasad P.V., and Venugopal S. Capability of a feed-forward artificial neural network to predict the constitutive flow behavior of as cast 304 stainless steel under hot deformation. J Eng Mater Technol 129 (2007) 242
18. Mandal S., Sivaprasad P.V., Venugopal S., and Murthy K.P.N. 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
19. Reidmiller M, Braun H. Proceedings of the international conference in neural, networks, San Francisco, CA, 1993. p. 586.
20. Abd El Meguid E.A., Gouda V.K., and Mahmoud N.A. Pitting corrosion behaviour of type SUS904L and SUS316L stainless steels in chloride solutions. Mater Trans Jpn Inst Met 35 (1994) 699
21. Malik A.U., Mayan Kutty P.C., Siddiqi N.A., Andijani I.N., and Ahmed S. The influence of pH and chloride concentration on the corrosion behaviour of AISI 316L steel in aqueous solutions. Corros Sci 33 (1992) 1809
22. Leckie H.P., and Uhlig H.H. Environmental factors affecting the critical potential for pitting in 18-8 stainless steel. J Electrochem Soc 113 (1966) 1262
23. Ruijin G., Srivastava S.C., and Ives M.B. Pitting corrosion behaviour of UNS N08904 stainless steel in a chloride/sulfate solution. Corrosion 45 (1989) 874
24. Wang J.H., Siu C.C., and Szklarska-Smialowska Z. Corrosion 44 (1988) 732
25. Hoar T.P., Mears D., and Rothwell G. The relationships between anodic passivity, brightening and pitting. Corros Sci 5 (1965) 279
26. Laycock N.J., and Newman R.C. Temperature dependence of pitting potentials for austenitic stainless steels above their critical pitting temperature. Corros Sci 40 (1998) 887
27. Bogaerts W, Van Haute A, Brabers MJ, Proceedings of the 8th international congress on metallic corrosion, vol. 1, Mainz, West Germany, 1981. p. 31.
28. - solution. Corros Sci 20 (1980) 597
29. Bianchi G, Cerquetti A, Mazza F, Torchio S. International conference on localized corrosion, Stachle RW, Brown BF, Kruger J, Agrawal A, editors, NACE, Houston TX, 1974. p. 399.
30. Hornik K., Stinchcombe M., and White H. Multilayer feed forward networks are universal approximators. Neural Netw 2 (1989) 359