The Corrosion Behaviour Of 10CrNi3MoV Steel And Q235 Steel In The Simulated Deep Sea With Corrison Prediction Model

The engineering structure materials which are on active service in shallow sea will withstand the greater test of corrosion resistance and mechanical properties,failure phenomena occurred frequently in the past.The long-term corrosion behavior of different metal materials in deep sea has become the research focus of the domestic and foreign researchers over the past few years,which could provide pivotal guiding significance.In this paper,with the simulated deep sea experiment and electrochemical workstation,the corrosion of two kinds of hull steel in the simulated deep sea environment has been studied by several experiments,such as weight loss measurement,polarization curve,electrochemical impedance spectroscopy,surface microstructure observation and corrosion product elemental analysis,etc.Through these works,exploring corrosion regulation and mechanism of two hull steels under different period,discussing the effects of alloy elements on the corrosion behavior.Applying the artificial neural network technology to the deep sea corrosion based on the previous work,through a large number of experimental data,using the BP artificial neural network to create prediction model of corrosion rate with material composition factors and environmental factors,mapping relationship between dual factors and hull steel corrosion rate.With the extension of soaking time,two kinds of hull steel's weight curve dropt rapidly at first,then the curve of hull steel 10CrNi3MoV gradually turned slow,but Q235 changed little,electrochemical impedance spectra(EIS)showed 10CrNi3MoV have pitting tendency,the electro-deposition of corrosion product decreased the corrosion of hull steel,the corrosion reaction process gradually changed from simple anodic dissolution into complex phase transformation,dissolution and redeposition of the film.The corrosion product was observed under the scanning electron microscopy(SEM),it was found that flocculent iron oxide appear on the edge of corrosion product active point,which play an important role in the film density of hull steel 10CrNi3MoV,but the corrosion product of Q235 has no flocculent iron oxide.Comparing with the results of x-ray diffraction(XRD)between lOCrNiMoV and Q235,it is found that 10CrNi3MoV have more diffraction peaks than Q235,higher relative peak intensity in ?-FeOOH than in Q235,and some chromium,nickel,molybdenum atoms occupied ferric site in the lattice;In the corrosion product of Q235,some magnesium atom occupied ferrous site in the lattice.Data analyzed by scanning electron microscope(SEM)and energy spectrum(SEM)on the section of corrosion product showed that corrosion product of lOCrNiMoV is more orderly,which have a good adhesion,high density but bigger corrosion pitting;The corrosion film of Q235 has a poor adhesion but no pitting,and it can be easily found desorption phenomenon.According to the data collected,it showed that 10CrNiMoV has a better corrosion resistance than Q235 in simulated deep sea environment but more risk of pitting.Two hundred groups of corrosion data with five kind of hull steal were collected,the artificial neural network is established after the optimization,with high corrosion prediction accuracy and generalization ability.Finally,the relationship model between the steel corrosion rate and dual factors is found.