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This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

This paper aim to investigate the influence of PA on the corrosion behavior of carbon steel with blast cleaned or pre-rusted treatments, and interpret the inhibition mechanism of PA on the steel with different surface treatments.

The influence of PA on the corrosion behavior of blast cleaned or rusty steel was investigated by means of electrochemical impedance spectroscopy (EIS). The EIS data were analyzed using the @ZsimpWin commercial software. The morphology and component of steel after immersion were characterized by scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), Fourier transformation infrared (FTIR) and X-ray diffractometer (XRD).

EIS analysis results indicated that PA had good corrosion inhibition for blast cleaned or rusty steel. SEM, EDS, FTIR and XRD further indicated that PA had two main corrosion inhibition processes for the corrosion inhibition of blast cleaned or rusty steel: corrosion dissolution and formation of protective barrier layers.

Most published works focus the attention only toward the effect of corrosion inhibitor for the clean metal surfaces. However, the surface condition of metal sometimes is unsatisfactory in the practical application of corrosion inhibitor, such as existing residual rust. Some studies also have shown that several corrosion inhibitors could be applied on partially rusted substrates. These inhibitors mainly include tannins and phosphoric acid, but not PA. Therefore, the authors investigated the influence of PA on the corrosion behavior of carbon steel with blast cleaned or pre-rusted treatments in this paper.

The study aims to investigate the anticorrosion performance of epoxy coatings using modified cerium oxide (CeO₂) by terephthalic acid (CeO₂-t) and fumaric acid (CeO₂-f) as corrosion inhibitors.

The chemical state of CeO₂-t, and CeO₂-f were analyzed by infrared radiation (IR) and X-ray diffraction (XRD). The effect of different inhibitors on the coating properties was analyzed by neutral salt spray tests (NSST) and electrochemical impedance spectroscopy (EIS).

The results of IR and XRD illustrate that the modification of CeO₂ was successful, and fumaric acid underwent a ring-opening reaction with epoxy resin (EP) in the coatings. The results of NSST and EIS showed that the coatings containing CeO₂-f exhibited the best anticorrosion performance.

CeO₂ is an effective inhibitor of the organic coatings. When surface modified, it is chemically bonded to EP, enhancing the anticorrosion performance of EP.

This paper aims to prepare a residual rust epoxy coating by adding different quantities of phytic acid (PA) on the surface of the rusty steel and investigate the corrosion protection of PA and its action mechanisms.

A residual rust epoxy coating by adding different quantities of PA was prepared on the surface of the rusty steel. The influence of PA on the corrosion resistance of epoxy-coated rusty steel was investigated by means of electrochemical impedance spectroscopy and adhesion testing.

Results indicated that PA can substantially improve the corrosion resistance of epoxy-coated rusty steel. This improvement is due to the reaction of PA with residual rust and generation of new compounds with protection properties and increased adhesive strength effects on the coating/metal interface. The coating showed better protection performance when 2 per cent PA was added.

Considering the structure of the active groups, PA has strong chelating capability with many metal ions and can form stable complex compounds on the surface of a metal substrate, thereby improving corrosion resistance. In recent years, PA has been reported to be useful in the conversion of coatings or as green corrosion inhibitor. To the best of the authors’ knowledge, few studies have reported the use of PA as a rust converter or residual rust coating. The present work aims to improve the corrosion resistance of residual rust epoxy coating by adding PA.

The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.

The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed.

The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly.

A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.