CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks

by Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 4
Year of Publication: 2012
Authors: Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta
10.5120/9678-4105

Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta . Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks. International Journal of Computer Applications. 60, 4 ( December 2012), 4-8. DOI=10.5120/9678-4105

@article{ 10.5120/9678-4105,
author = { Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta },
title = { Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 4 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number4/9678-4105/ },
doi = { 10.5120/9678-4105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:43.128755+05:30
%A Haider M. Mohammad
%A Nawal J. Hammadi
%A Rafil M. Lafta
%T Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 4
%P 4-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Precorroded steel A-106 B specimens were prepared at different surface roughness. These specimens were immersed in corrosive ferric chloride solution in different concentrations (1. 5, 3. 0, 4. 5, 6. 0% wt. ) at specified durations to initiate primarily the pitting corrosion. The corrosion pits distribution depend on the corrosive concentration, degree of surface roughness, and immersion duration. The pits were characterized using metallurgical microscope. Also, The pitting characteristics aimed to be predicted by "Artificial Neural Networks" (ANNs). The results obtained of pit quantification by ANNs predictions are shown to be agreed well against experimental values. i. e. R2=0. 9839

References
  1. Dooley, E. J. , Lee, B. , and Wei R. P. 2000. The effect of pitting corrosion on fatigue life. Fatigue Fracture Eng. Mat. Struct. 23, pp 555-560.
  2. Sankaran, K. K. , and Jata, K. V. 2001. Effect of pitting corrosion on fatigue behaviour. modeling and experimental studies, Mat. Sci. Eng. A297, P 223.
  3. Frantziskonis, G. N. , Simon, L. B. , Woo. J. , and Matikas. T. E. 2000. Multiscale characterization of pitting corrosion and application. Eur. J Mech. A-Solid 19, pp 309-318.
  4. Chen, G. S. , Lia, C. M. , Wan, K. C. , Gao, M. , and Wei, R. P. 1997. Pitting corrosion & fatigue crack nucleation in: Effect of environment on initiation of crack growth. ASTM 1298, Am. Soc. For Tesing & Material, pp 18-31.
  5. Piascik, R. S. , and Willard, S. A. 1994. The growth of small corrosion fatigue crack. Fatigue Fracture Engg. Mat. Structure 17 (11), pp 1247-1259.
  6. Van der Walde, Brokenbrough, J. R. , Craig, B. A. , and Hillberry. 2005. Multiple fatigue crack growth in pre-corroded alloy. Int. J Fatigue 27, pp 1509-1518.
  7. Johnes, K. 2004. Effect of microstructure on pit-to-crack transition of Al-alloys. Phd thesis, Univ. of Utah.
  8. Perez, R. 1996. Corrosion/Fatigue Metrics In: Proc. of ICAF 97 Symp on Fatigue in New and ageing aircraft. Edinburgh, Scotland, pp 215-290.
  9. Tue gel, E. J. 2003. Investigation of the effect of corrosion pits on fatigue life of Al-structure. Rpt. AFL-VA-WP-TR (2003), AFR Lab. , Wright-Potterson, Ohio.
  10. Codaro, E. N. , Nakazato, R. Z. , Horovistiz, A. L. , Ribeiro, L. M. F. , Ribeiro, R. B. , and Hein, L. R. D. 2002. Characterization & evaluation of pitting corrosion. Mat. Sc. Eng. A334, pp 298-306.
  11. ASTM International. 2008. Standard Specification for seamless carbon steel pipe for high-temperature service.
  12. Standard Guide for Examination and Evaluation of Pitting Corrosion. ASTM 2005.
  13. Van Der Sluys, Piascik, R. S. , Zawierucha, R. 1997. Effect of the environment on the initiation of crack growth. ASTM STP 1298, ASTM 1997.
  14. Rojas, R. 1996. Neural Networks: A Systematic Introduction. Springer.
  15. Baughman, D. R. and Liu, Y. A. 1995. Neural Network in Bioprocessing and Chemical Engineering. Academic Press, San Diego, CA.
  16. Simpson, P. 1990. Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. Pergamon Press, New York, N. Y.
Index Terms

Computer Science
Information Sciences

Keywords

Pitting corrosion Artificial neural network ANN and surface roughness

Powered by PhDFocusTM