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Predicting SCC Behavior of Austenitic Stainless Steels in High Temperature Water by Artificial Neural Network |
Hao Guo;Zhanpeng Lv;Guoqiang Feng;Xun Cai;Wu Yang |
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Abstract Two kinds of empirical learning methods based on artificial neural network(ANN),i.e., double layer perceptron(DLP) model and Elman feedback(EF) model,have been used to analyze SCC data and predict the SCC susceptibitlity of austenitic stainless steels in high temperature water(HTW). The results indicated that DLP model could not converge after long training epochs while EF model could reach a steady value within limited training epochs for the SCC data of stainless steels(SS).The SCC susceptibility fo 304SS and 316SS in HTW depends on the parameters such as temperature(T), dissolved O2 content(DO), chloride ion content ([Cl-]) and electrode potential(E). The threshold value(ThV) for SCC used in the EF model affected the prediction ratios. For THV<=0.6, the ranges of prediction ratio were ca.0.66-0.90for method Ⅰ(including the data to be predicted) and 0.60~0.79 for method Ⅱ(excluding the data to be predicted) for 304SS, ca.0.81~0.98 for method Ⅰ and 0.78~0.90 for method Ⅱ for 316SS. The curves of mean value of prediction ratios show that the prediction ratios have the characteristics of normal distribution and the best ThV is 0.5. The EF model is a very useful tool for qualitatively predicting the SCC behaviour of austenitic stainless steels in HTW.
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Received: 08 July 2003
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