Prediction of Time to Failure in SCC of 304 Stainless Steel in Aqueous Chloride Solution Using Neural Network
Authors
Abstract
Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an
effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show 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 exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.
effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show 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 exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.
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