Stainless steel has proved to be an important material to be used in a wide range of applications. For this reason, ensuring the durability of this alloy is essential. In this work, pitting corrosion behaviour of EN 1.4404 stainless steel is evaluated in marine environment in order to develop a model capable of predicting its pitting corrosion status by an automatic way. Although electrochemical techniques and microscopic analysis have been shown to be very useful tools for corrosion studies, these techniques may present some limitationus. With the aim to solve these drawbacks, a three-step model based on Artificial Neural Networks (ANNs) is proposed. The results reveal that the model can be used to predict pitting corrosion status of this alloy with satisfactory sensitivity and specificity with no need to resort to electrochemical tests or microscopic analysis. Therefore, the proposed model becomes a useful tool to predict the behaviour of the material against pitting corrosion in saline environment automatically.
The atmospheric corrosion of metallic materials causes great economic loss every year worldwide. Thus, it is meaningful to predict the corrosion loss in different field environments. Generally, the corrosion prediction method includes three parts of work: the modelling of the corrosive environment, the calibration of the corrosion effects, and the establishment of the corrosion kinetics. This paper gives an overview of the existing methods as well as promising tools and technologies which can be used in corrosion prediction. The basic corrosion kinetic model is the power function model and it is accurate for short-term corrosion process. As for the long-term corrosion process, the general linear models are more appropriate as they consider the protective effect of the corrosion products. Most corrosion effect models correlate the environmental variables, which are characterized by the annual average value in most cases, with corrosion parameters by linear equations which is known as the dose-response function. Apart from these conventional methods, some mathematical and numerical methods are also appropriate for corrosion prediction. The corrosive environment can be described by statistical distributions, time-varying functions and even geographic information system (GIS), while the corrosion effect can be captured via response surface models and statistical learning methods.
The damage caused by marine fouling organisms to ships and underwater artificial equipment is becoming increasingly serious issue, and the prevention and control of marine biofouling has always been a research hotspot in marine coatings. Aiming at the problems of poor adhesion, long curing time and high curing temperature of low-surface energy marine antifouling coatings of organosilicon, a hydrophobic low-surface energy nano-SiO2/silicon acrylic resin nanocomposite coating was synthesized. The anticorrosive property of the composite coatings was analyzed by simulated seawater periodic immersion experiments. The gel permeation chromatography analysis showed that polydimethyl-siloxanes (PDMS) is involved in cross-linking reactions. The dynamic thermomechanical analysis indicated that the glass transition temperature of resin is 58 °C. The contact angle (CA) test showed that the CA of nanocomposite coating is 109.99°. All the detection results can support the excellent antifouling and anticorrosion performance of the low surface energy nanocomposite coatings.
In a primer coating system used in aerospace applications to protect aluminum alloy substrate, praseodymium is added as corrosion inhibitors while CaSO4 is primarily added as filler materials. The interaction of Pr and CaSO4 is unknown. The goal of this study is to characterize any cooperative or synergistic inhibition between these two. Cooperative inhibition can be defined when one inhibitor enhances inhibiting effect of the other that already has inhibiting ability. Synergistic inhibition can be defined when one inhibitor activates the inhibiting effect of the other that originally does not inhibit. Optical profilometry, electrochemical techniques and X-ray photoelectron spectroscopy were used to characterize corrosion results. The results showed that several pit parameters will affirm the inhibition effect. Electrochemical results cannot always detect modest corrosion inhibitors. Cooperative inhibition was detected in pH 5 while synergistic inhibition was observed in pH 8. Synergistic inhibition occurs because SO42− helps with gelation of Pr to passivate the surface.
In this work, results on the causes that could promote the abnormal spallation of the oxides formed on the surface of high-strength low-alloy (HSLA) steels are presented. By means of Rietveld refining of X-ray diffraction spectra, scanning electron microscopy analyses and calculations, it was found that the value of the thermal stress experienced by the oxide scale reached a maximum when the oxide scale was comprised by 65% wt magnetite Fe3O4 and 24% wt wustite FeO this, due to the incomplete transformation of the latter phase to Fe3O4 and α-Fe from cooling from 670 °C to ambient temperature. Contrarily, it was found that when a balance in the amount of Fe3O4 and FeO was 46.4 and 46.5%wt respectively, the calculated thermal stress was reduced, and oxide spallation was not that severe. The reasons for oxide scale detachment from the surface of the steels are explained in terms of the adhesion energy of the bulk oxide scale, the amount of magnetite Fe3O4 present in the oxides and the chemical composition of the steel particularly the elements chromium and titanium.
In order to evaluate the seawater corrosivity of typical sea areas in China and provide guidance for the seawater corrosion protection on marine equipment and facilities, field exposure test was carried out. These typical sea areas under various climatic zones in China included Qingdao, Zhoushan, Sanya and a South China Sea reef, and Q235, copper, 5083 aluminum alloy and 304 stainless steel were chosen as test materials. The continuous monitoring of seawater environmental factors (temperature, salinity, pH, dissolved oxygen, etc.,) and the statistical work of first-year corrosion rates of test materials were done. Then, based on the metal corrosion rates method and the environmental factors method, the seawater corrosivity of these typical sea areas in China were classified, respectively. Furthermore, the classification results from the two methods were compared and analyzed.