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prediction of time to failure in stress corrosion cracking of 304 stainless steel in aqueous chloride solution by artificial neural network

Lajevardi, S. A ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1134/S2070205109050207
  3. Publisher: 2009
  4. Abstract:
  5. Despite the numerous researches in Stress Corrosion Cracking (SCC) risk of austenitic stainless steels in aqueous chloride solution, no formulation or reliable method for prediction of time to failure as a result of SCC has yet been defined. In this paper, the capability of artificial neural network for estimation of the time to failure for SCC of 304 stainless steel in aqueous chloride solution together with sensitivity analysis has been expressed. The output results showed that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also demonstrated the effects of input parameters (Temperature, Applied stress and Cr concentration) on SCC of 304 stainless steel in aqueous chloride solutions. ©Pleiades Publishing, Ltd., 2009
  6. Keywords:
  7. 304 stainless steel ; Applied stress ; Aqueous chloride ; Artificial Neural Network ; Cr concentration ; Experimental data ; Input parameter ; Time to failure ; Austenitic stainless steel ; Chlorine compounds ; Chromium ; Neural networks ; Quality assurance ; Residual stresses ; Sensitivity analysis ; Ternary systems ; Stress corrosion cracking
  8. Source: Protection of Metals and Physical Chemistry of Surfaces ; Volume 45, Issue 5 , 2009 , Pages 610-615 ; 20702051 (ISSN)
  9. URL: https://link.springer.com/article/10.1134/S2070205109050207