An Investigation on Application of Experimental Design and Linear Regression Technique to Predict Pitting Potential of Stainless Steel
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This study using experimental design and linear regression technique was implemented in order to predictthe pitting potential of stainless steel in marine environments, with the target materials being AL-6XN andSTS 316L. The various variables (inputs) which affect stainless steel’s pitting potential included the pit ting resistance equivalent number (PRNE), temperature, pH, Cl-concentration, sulfate levels, and nitratelevels. Among them, significant factors affecting pitting potential were chosen through an experimentaldesign method (screening design, full factor design, analysis of variance). The potentiodynamic polariza tion test was performed based on the experimental design, including significant factor levels. From thesetesting methods, a total 32 polarization curves were obtained, which were used as training data for the lin ear regression model. As a result of the model’s validation, it showed an acceptable prediction perfor mance, which was statistically significant within the 95% confidence level. The linear regression modelbased on the full factorial design and ANOVA also showed a high confidence level in the prediction of pit ting potential. This study confirmed the possibility to predict the pitting potential of stainless steel accord ing to various variables used with experimental linear regression design.
Abstract
This study using experimental design and linear regression technique was implemented in order to predictthe pitting potential of stainless steel in marine environments, with the target materials being AL-6XN andSTS 316L. The various variables (inputs) which affect stainless steel’s pitting potential included the pit ting resistance equivalent number (PRNE), temperature, pH, Cl-concentration, sulfate levels, and nitratelevels. Among them, significant factors affecting pitting potential were chosen through an experimentaldesign method (screening design, full factor design, analysis of variance). The potentiodynamic polariza tion test was performed based on the experimental design, including significant factor levels. From thesetesting methods, a total 32 polarization curves were obtained, which were used as training data for the lin ear regression model. As a result of the model’s validation, it showed an acceptable prediction perfor mance, which was statistically significant within the 95% confidence level. The linear regression modelbased on the full factorial design and ANOVA also showed a high confidence level in the prediction of pit ting potential. This study confirmed the possibility to predict the pitting potential of stainless steel accord ing to various variables used with experimental linear regression design.
참고문헌 (13)
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