This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low...This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.展开更多
Machine-learning and big data are among the latest approaches in corrosion research.The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment.Corrosion big ...Machine-learning and big data are among the latest approaches in corrosion research.The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment.Corrosion big data is the application of mathematical methods to huge amounts of data to find correlations and infer probabilities.It is possible to use corrosion big data method to distinguish the influence of the minimal changes of alloying elements and small differences in microstructure on corrosion resistance of low alloy steels.In this research,corrosion big data evaluation methods and machine learning were used to study the effect of Sb and Sn,as well as environmental factors on the corrosion behavior of low alloy steels.Results depict corrosion big data method can accurately identify the influence of various factors on corrosion resistance of low alloy and is an effective and promising way in corrosion research.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52104319 and 52374323)。
文摘This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.
基金financially supported by the Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing(No.2022 B H003)。
文摘Machine-learning and big data are among the latest approaches in corrosion research.The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment.Corrosion big data is the application of mathematical methods to huge amounts of data to find correlations and infer probabilities.It is possible to use corrosion big data method to distinguish the influence of the minimal changes of alloying elements and small differences in microstructure on corrosion resistance of low alloy steels.In this research,corrosion big data evaluation methods and machine learning were used to study the effect of Sb and Sn,as well as environmental factors on the corrosion behavior of low alloy steels.Results depict corrosion big data method can accurately identify the influence of various factors on corrosion resistance of low alloy and is an effective and promising way in corrosion research.
