Improvement of corrosion resistance of maraging steel manufactured by selective laser melting through Intercritical heat-treatment

Existing studies suggest that martensite-to-austenite reversion can increase the overall mechanical strength of maraging steel. Their effect on corrosion properties, however, is unclear. Selective laser melted (SLM) specimens were tempered near austenite finish temperatures to investigate the electrochemical effect of reversed austenite. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectrometry (EDS) were used to characterize their microstructure. To define and test pitting performance, potentiodynamic polarization and open-circuit potential were performed in a 3.5 wt. % NaCl solution. The reversed austenite precipitated mainly along the martensite lath boundaries during the Intercritical heat treatment at 720°C. The nucleation of reversed austenite is allowed by the local Ni enrichment caused by the dissolution of intermetallic particles. As a result, the tempered 720°C specimens reported a higher pitting potential, lowest corrosion current density, and lowest corrosion rate than the as-printed, aged, and homogenized specimens. No investigations have been performed to date that demonstrate the impact of austenite reversion on the corrosion susceptibility of SLM maraging steel. Other than being nobler, austenite is lighter than martensite due to reduced precipitant density, accounting for fewer galvanic cells and a lower corrosion rate.

A Novel Self-Healing Conversion Film Formed on Rebar in Solution Containing Cerium and Molybdenum Ions

A novel conversion film containing cerium and molybdenum was synthesized on the rebar surface, and then the pitting sensitivity of the rebar was improved in a concrete environment containing chloride. This conversion film presented a two-layer structure, which involved CeMoOx as the outer layer and FeOx as the inner layer. The film with two-layer structure exhibited a strong corrosion resistance after being immersed in an alkaline environment (pH=12.5) added with 0.1 M NaCl, and the charge transfer resistance (Rt) could reach 5.88×106 Ω·cm2 after immersion for 1600 h, which was approximately 2.5 times the initial Rt value. Moreover, it was found that the film has a self-heal property when being damaged beyond the pitting potential. The anticorrosion mechanism of the film and its self-healing mechanism were discussed in depth in this study.

The Effect of Heat Treatment on Microstructure, Microhardness and Pitting Corrosion of Ti6Al4V Produced by Electron Beam Melting Additive Manufacturing Process

There has been limited studies on corrosion behaviour of post-processed Electron Beam Melted (EBM) Ti6Al4V, given that the factors affecting corrosion resistance of AM Ti6Al4V remain unclear. This paper proposes using heat treatment method to improve the pitting corrosion resistance of EBM Ti6Al4V. Different treatment profiles alter the microstructure of EBM Ti6Al4V. A clear trend is observed between microhardness and α lath width. As-printed EBM Ti6Al4V exhibits an inferior pitting potential, while heat treatment provided a significant improvement in the corrosion resistance. This study finds that the β phase fraction is a better indicator than the α lath width for pitting corrosion resistance. Solution air-cooled & ageing heat treated EBM Ti6Al4V exhibits good mechanical and corrosion properties, and even performs better than commercial cast Ti6Al4V.

Tribological Properties and Corrosion Resistance Properties of NbN/TiN Multilayer, TiNb-NX Single-Layer and Coatings that are Doped with Carbon

NbN/TiN, TiNb-NX and CH-TiNb-N12 coatings are deposited by RF magnetron sputtering to determine the tribological properties and corrosion resistance. ‘x’ is the flux rate for nitrogen and ‘CH’ signifies the addition of acetylene. In terms of the corrosion resistance, all the coatings have a similar corrosion potential and NbN/TiN multilayer coatings exhibit the lowest corrosion current. The NbN/TiN multilayer has a low pitting potential so severe pitting corrosion is observed on the surface. CH-TiNb-N12 coating is most resistant to corrosion and exhibits no pitting before the test ends. In contact with counter-bodies with a Si3N4 ball or an AISI 52100 ball, a CH-TiNb-N12 coating acts as a solid lubricant so the wear mechanism shows the least abrasion. The CH-TiNb-N12 coating has the lowest wear rate and coefficient of friction for sliding against Si3N4 and AISI 52100 balls. The wear rate is respectively 3.2 and 6.8 times less than that for SKH51 substrate when sliding against Si3N4 and AISI 52100 balls. The results for this study show that a TiNb-N12-CH coating has the best tribological properties and corrosion resistance.

Effect of Ni Addition on the Corrosion Resistance of NiTi Alloy Coatings on AISI 316L Substrate Prepared by Laser Cladding

In this paper, an equiatomic NiTi (55NiTi) alloy powder was mixed with pure Ni powder to prepare laser cladding coatings on a 316L stainless steel substrate to study the effect of Ni addition on the microstructure and corrosion resistance of the coatings. The microstructure and phase composition of the coatings were analyzed using a scanning electron microscope (SEM) with configured energy-dispersive spectrometer (EDS) and X-ray diffractometer (XRD). OCP (open-circuit potential), PD (potentiodynamic polarization) and EIS (electrochemical impedance spectroscopy) experiments were conducted by a Gamry electrochemical workstation, and corresponding eroded morphologies were observed to evaluate the coating’s anti-corrosion performance. The addition of Ni led to fine and uniform dendrites and dense microstructure under the metallurgical microscope, which were beneficial for the formation of the passive film mainly consisting of titanium dioxide (TiO2). The results show that the pitting potential of the 55NiTi + 5Ni coating was 0.11 V nobler than that of the 55NiTi coating, and the corrosion current density was less than half that of the 55NiTi coating. The corrosion initiated preferentially at the interfaces of dendrites and inter-dendritic areas, then spread first to dendrites rather than in the inter-dendritic areas.

Low-Temperature Plasma Nitriding of 3Cr13 Steel Accelerated by Rare-Earth Block

The plasma nitriding of 3Cr13 steel occurred at 450 °C for 4, 8 and 12 h in NH3 with and without rare earth (RE). The nitrided layers were characterized using an OM, SEM, TEM, XRD, XPS, microhardness tester and electrochemical workstation. The modified layer, with and without La, are composed of a compound layer and diffusion layer from surface to core. After the addition of La during nitriding, the maximum increase of layer thickness, mass gain and average microhardness was 15.6%, 35.8% and 212.50HV0.05, respectively. With the increase of the proportion of ε-Fe2-3N, the passivation zone of the corrosion resistance curve increases from 2.436 to 3.969 V, the corrosion current density decreases, the corrosion potential and pitting potential both increase, and, consequently, the corrosion resistance is significantly improved. Most of the surface microstructures of the nitrided layer was refined by the addition of La. The presence of La reduces the N content in the modified layer, which accelerates the diffusion of N atoms and, thus, accelerates the nitriding process.

Regularities of the Influence of Substitutional and Interstitial Alloying Elements on the Corrosion Properties of Austenitic Stainless Steels

Pitting corrosion studies were carried out on nitrogen containing austenitic stainless steels of different compositions and concentrations of alloying elements. As was shown there is a certain predicted influence of the concentration of each alloying elements as chromium, manganese, nickel, carbon and nitrogen on the pitting potential (Eb) of investigated steels with the nitrogen content less than 0.169 wt. %. However with an increase of the nitrogen content to a certain value (in our study up to 0.82 wt. %) the predicting of alloying elements influence on pitting potential of the steels requires a new approach. Based on the analysis of the experimental results and to take into account the influence of all alloying elements in steel on the pitting potential, a regression equation is proposed. In the presence of nitrogen, the positive role of carbon on the pitting resistance of stainless steel was shown, and the critical values of the total content (C + N) and the C / N ratio were determined, allowing prediction of the best composition of stainless steel.

Pitting corrosion prediction from cathodic data: application of machine learning

Purpose To constitute input data, the authors carried out electrochemical experiments. The authors performed voltammetric scans in a very cathodic potential region. The authors constituted an experimental table where for each experiment we note the current values recorded at a low polarization range and the pitting potential observed in the anodic region. This study aims to concern carbon steel used in a nuclear installation. The properties of the chemical solutions are close to that of the cooling fluid used in the circuit. Design/methodology/approach In a previous study, this paper demonstrated the effectiveness of machine learning in predicting the localized corrosion resistance of a material by considering as input data the physicochemical properties of its environment (Boucherit et al., 2019). With the present study, the authors improve the results by considering as input data, cathodic currents. The reason of such an approach is to have input data that integrate both the surface state of the material and the physicochemical properties of its environment. Findings The experimental table was submitted to two neural networks, namely, a recurrent network and a convolution network. The convolution network gives better pitting potential predictions. Results also prove that the prediction by observing cathodic currents is better than that obtained by considering the physicochemical properties of the solution. Originality/value The originality of the study lies in the use of cathodic currents as input data. These data contain implicit information on both the chemical environment of the material and its surface condition. This approach appears to be more efficient than considering the chemical composition of the solution as input data. The objective of this study remains, at the same time, to seek the optimal neuronal architectures and the best input data.