Degradation experiments and analysis of corrosion damage

International Journal of Damage Mechanics

Volumn 19, Issue 2, 2010, Pages 197-210

  Pidaparti, Ramana M ^a   Koombua, Kittisak ^a   Abdelsayed, Ihab ^a   Sharma, Himanshu ^b  

^a Virginia Commonwealth University   (United States)

^b Wisconsin Electric Machines and Power Electronics Consortium   (United States)

Author keywords

Aircraft aluminum; Analysis; Artificial neural networks; Corrosion; Degradation; Prediction

Indexed keywords

AA 2024; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; CORROSION DAMAGE; CORROSION DEGRADATION; CORROSION GROWTH; CORROSION PROCESS; DAMAGING MECHANISM; ELECTROCHEMICAL CONDITIONS; ELECTROCHEMICAL PARAMETERS; ENERGY DISPERSIVE SPECTROMETRY; ENGINEERING STRUCTURES; EXPERIMENTAL DATA; EXPOSURE-TIME; FUNCTION OF TIME; MATERIAL LOSS; SIMULATION MODEL;

ALUMINUM; BIODEGRADATION; CHEMICAL ANALYSIS; CHEMICALS; CORROSION PREVENTION; DEGRADATION; EXPERIMENTS; FORECASTING; GROWTH (MATERIALS); NEURAL NETWORKS; SPECTROMETRY;

CHEMICAL ELEMENTS;

EID: 77949421791     PISSN: 10567895     EISSN: 15307921     Source Type: Journal    
DOI: 10.1177/1056789508098586     Document Type: Article

References (23)

1. Aleksander, I. and Morton, H. (1995). An Introduction to Neural Computing. International Thomson Computer Press, London.
2. Anderson, D. and McNeill, G.A. (1992). Artificial Neural Networks Technology, Rome Laboratory, Rome, New York.
3. Beale, R. and Jackson, T. (1990). Neural Computing: An Introduction, Institute of Physics Publishing Ltd., Bristol, UK.
4. Brown, W.M., Gedeon, D.T. and Groves, D.I. (2003). Use of Noise to Augment Training Data: A Neural Network Method of Mineral-Potential Mapping in Regions of Limited Known Deposit Examples, Natural Resources Research, 12: 141-152.
5. Bryson, A.E. and Ho, Y.C. (1969). Applied Optimal Control, Blaisdell, New York.
6. Bulsari, A.B. and Kallio, S. (1995). Proceedings of the International Conference on Engineering Applications of Neural Networks.
7. Burstein, G.T., Liu, C., Souto, R.M., and Vines, S.P. (2004). Origins of pitting corrosion, Corrosion Engineering, Science and Technology, 39: 25-30.
8. Frantiziskonis, G.N., Simon, B.L., Woo, J. and Matikas, T.E. (2000). Multiscale characterization of pitting corrosion and application to an aluminum alloy, European Journal of Mechanics - A/Solids, 19: 309-318.
9. Hoeppner, D.W. (1979). American Society for Testing and Materials (ASTM). Fatigue Mechanisms, STP, 675, Philadelphia, PA.
10. Hua, J., Lowey, J., Xiong, Z. and Dougherty, E.R. (2006). Noise-injected neural networks show promise for use on small-sample expression data, BMC Bioinformatics, 7: article no. 274.
11. Kim, S.T., Tadjiev, D. and Yang, H.T. (2006). Fatigue Life Prediction under Random Loading Conditions in 7475-T7351 Aluminum Alloy using the RMS Model, International Journal of Damage Mechanics, 15: 89-102.
12. Lai, M.O. (1993). Fatigue Performance of Repaired Aluminum Panels, International Journal of Damage Mechanics, 2: 199-204.
13. Malki, B. and Baroux, B. (2005). Computer simulation of the corrosion pit growth, Corrosion Science, 47: 171-182.
14. Marcus, P. and Oudar, J. (Eds) (1995). Corrosion Mechanisms in Theory and Practice, Marcel Dekker, Inc., New York.
15. Orchard, G.A. (1991). Neural Computation: A Beginner's Guide, Psychology Press Ltd., London.
16. Pidaparti, R.M., Palakal, M.J. and Fang, L. (2005). Cellular Automata Approach to Aircraft Corrosion Growth, International Journal on Artificial Intelligence Tools, 14: 361-369.
17. Principe, J.C., Euliano, N.R. and Lefebvre, W.C. (2000). Neural and Adaptive Systems: Fundamentals Through Simulations, John Wiley & Sons Inc., New York.
18. Russell, P. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach, Prentice Hall Inc., Upper Saddle River, New Jersey.
19. Shreie, L.L., Jarman, R.A. and Burstein, C.T. (1994). Corrosion - Metal/Environmental Reactions, Butterworth & Heinemann Ltd, Oxford.
20. Strehblow, H.H. (1995). Corrosion Mechanisms in Theory and Practice, Marcel Dekker, Inc., New York.
21. Swingler, K. (1996). Applying Neural Networks, A Practical Guide, Academic Press Inc., London.
22. Wallace, W. and Hoeppner, D.W. (1985). AGARD Corrosion Handbook Volume I: Corrosion-Causes and Case Histories, AGARD-AG-278, AGARD, Neuilly-sur-Seine & NTIS, Springfield, Virginia.
23. Wei, R.P., Liao, C. and Gao, M. (1998). A transmission electron microscopy study of constituent-particle-induced corrosion in 7075-T6 and 2024-T3 aluminum alloys, Metallurgical and Materials Transactions A, 29: 1153-1160.