Cover
Vol. 11 No. 1 (2011)

Published: May 31, 2011

Pages: 82-88

Original Article

Artificial Neural Network Prediction Model for Impact Energy of Thermal Aged Cast Stainless Steel

Haider M. Mohammed 1
1 College of Engineering – Basrah University
History
  • Received: February 28, 2011
  • Available Online: May 31, 2011
DOI

https://doi.org/10.33971/bjes.11.1.7

Abstract

Impact energy prediction of thermal aged cast stainless steel from impact test was studied using artificial neural network (ANN) modeling. Impact energy data for specimens from eleven cast stainless steel alloys at different aging times and temperatures, were used to evaluate possible artificial neural network architecture for prediction impact energy. These data are taken from Argonne National Laboratories (ANL) in USA that involved impact test results of cast stainless steel after aging between 200 and 400oC for up to 30000 hour. The ANN model exhibited excellent comparison with experimental results of ANL i.e. correlation coefficient (R=0.9451) and mean square error (MSE=1.2*10-5). Since a large number of variables were used during training the ANN model, a reliable and useful predictor for impact energy in thermal aged cast stainless steel was provided.

References

  1. Butler S., “Semi-probabilistic fracture mechanics approach for quality assurance system for welds of a containment for a 1000 MW nuclear power unit”, Nuclear Engineering and Design, Vol. 174, pp177-187, 1997
  2. Ipohorski M., “Failure analysis of a steam valve stem”, Materials Characterization, Vol. 50, pp23-30, 2003
  3. Przemyslaw C., “Fracture toughness of X10CrMoVNb91(P-91) steel after aging”, Metal J., Vol.24, p1, 2005
  4. Chopra O. K. & Sather A., “Initial assessment of the mechanism and significance of low-temperature embrittlement of cast stainless steels in LWR system”, NUREG/CR-5385, ANL-89/17, 1990
  5. Chester M., “Neural networks tutorial”, Englewood Cliffs, NJ: Prentice Hall, 1993.
  6. Sudhakar K. & Haque M., “Effect of heat treatment on mechanical properties and neural network simulation in MIM alloy”,. Braz J Mater Sci Eng, Vol.3, pp5-14, 2000.
  7. Iacoviello F., Iacoviello D., and Cavallini M., “Analysis of stress ratio effects on fatigue propagation in a sintered duplex steel by experimentation and artificial neural network approaches”, International Journal of Fatigue, Vol. 26, pp 819–828, 2004.
  8. Mathew M. D., Dae W. K., and Woo S. R., “A neural network model to predict low cycle fatigue life of nitrogen- alloyed 316L stainless steel”, Materials Science and Engineering, Vol. A 474, pp. 247–253, 2008.
  9. Duo O., Yongchang P., and Atilla I., “Artificial neural networks and their application to assessment of ultimate strength of plates with pitting corrosion”, Ocean Engineering, Vol. 34, pp. 2222–2230, 2007.
  10. Ramana K. V. , “Effect of different environmental parameters on pitting behavior of AISI type 316L stainless steel: Experimental studies and neural network modelling”, Materials and Design journal, Vol. 30, pp 3770–3775, 2009.
  11. Haykin S., “Neural networks”, New York: Macmillan College Publishing Company, Inc., 1994.
  12. Kang J. & Song J., “Neural network applications in determining the fatigue crack opening load”, Int J Fatigue, Vol.20, pp 57–69, 1998.
  13. Rao H. & Mukherjee A., “Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix components”, Comput Mater Sci, Vol.5, pp 307–322, 1996.
  14. Chopra O. K., & Chung H. M., “Initial Assessment of the Processes and Significance of Thermal Aging in Cast Stainless Steels”, NUREG/CP–0097 Vol. 3, p. 519, 1989.