
Marine Corrosion of Steels
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Bingqin Wang is an assistant researcher at the Institute of Advanced Materials snd Technology, University of Science and Technology Beijing. After earning his PhD degree from University of Science and Technology Beijing, he has been engaged in research work centered on corrosion big data technology. He has discovered the dynamic evolution law of weathering steel rust layers and the critical temperature of atmospheric corrosion that affects the protective performance of rust layers. Additionally, he has developed corrosion image big data technology, successfully achieving atmospheric corrosion prediction under multi-modal conditions and quantification of the protective performance of rust layers. He has published more than 20 SCI papers and won several scientific awards.
Xiaogang Li is a professor at the University of Science and Technology Beijing. He holds several leadership roles in prominent academic and professional organizations. His research interests include material corrosion theory, development of corrosion-resistant new steel grades, and improving the performance of traditional weathering steel. He has received many scientific and technological awards, including the NACE International Outstanding Engineering Contribution Award and the National Outstanding Engineer Award.
Shasha Zhang is an engineer at the School of Advanced Engineering at the University of Science and Technology Beijing. Based on the national artificial intelligence technology development strategy and the actual needs of material corrosion protection, she has been dedicated to research in artificial intelligence and Internet of Things (IoT) technology, the application of artificial intelligence technology in material corrosion detection, and the theory and technology of corrosion big data. Her academic contributions include three published papers, two authored books, and multiple awards in science and technology competitions.
Zhong Li is an associate professor in the Institute for advanced Materials and Technology, University of Science and Technology Beijing. Her graduated from Harbin Engineering University for Bachelor of Science, University of Calgary for Master degree and Ohio University for Ph. D degree. She was employed in Institute of corrosion control systems engineering. Her research work focusses on the theory and behavior of microbiologically induced corrosion, theory and behavior of stress corrosion cracking, the theory and technology of corrosion big data and the research and development of advanced materials for microbiologically induced corrosion resistance. She has published more than 20 SCI papers and won several scientific awards.
Inhalt
Chapter 2.Corrosion fatigue behavior of high manganese steel in atmospheric environment.
Chapter 3.Effect of Microalloying Elements on The Corrosion Resistance of Low-Density Steel.
Chapter 4. Coupling of Multiple Corrosion in the Corrosion Process of Titanium-Steel Composites .
Chapter 5.Effects of Corrosion Inhibitors and Flow rate on the Corrosion Resistance of Ductile Iron Pipes.
Chapter 6. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Rebar Corrosion Resistance Modulation.
Chapter 7.Application of Novel Big Data Intelligent Corrosion Assessment Approach in blast furnace gas pipe steel corrosion resistance Analysis.
Chapter 8. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion-Resistant Low Alloy Steel Development.
Chapter 9. Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion Prediction and Data Mining Modeling.
Chapter 10. Perspectives on the Application of Artificial Intelligence in Investigating Corrosion Mechanisms of Steel and Designing Corrosion-Resistant Alloys.
Chapter 1
Stress Corrosion Behavior of High-manganese Steel in Polluted Marine Atmospheric Environments
1.1 Introduction
China's railways are advancing toward higher speeds and heavier loads, and railway steels are also evolving toward higher corrosion and cracking resistance, longer lifespan, and other advanced properties. As a classic material for railways, high-manganese steel exhibits high strength, high ductility, and high strain hardening ability, making it irreplaceable by any other material to date [1-8].
The high operational stresses generated during train operation, combined with corrosive environments, can lead to varying degrees of corrosion damage in railway steels. Researchers domestically and internationally generally focus on the mechanical properties and deformation mechanisms of high-manganese steel, while in-depth studies on its corrosion issues are lacking. During service, high-manganese steel experiences high operational stresses, causing harmful elements to concentrate on and within the metal surface [9-14]. Coupled with the effects of a corrosive environment, the tendency for stress corrosion cracking (SCC) in high-manganese steel increases [6-15]. Although China has been able to produce austenitic high-manganese steel with a high yield-to-tensile strength ratio, which can well meet the operational requirements of railways in terms of structure and performance, its corrosion resistance and resistance to SCC under long-term exposure to industrial atmospheric conditions remain to be tested [16-28]. This poses constraints on the safety and development of China's railway industry. High-manganese steel plays a crucial role in the railway industry, and understanding its stress corrosion behavior and mechanisms is vital for ensuring railway safety and promoting development [19, 20]. When evaluating the corrosion of high-manganese steel, multiple variables such as alloying elements and environmental factors are typically considered comprehensively. The rust layer is also a critical factor influencing corrosion and its importance cannot be underestimated [29-34]. Especially in industrial atmospheric environments containing chloride ions (Cl-), corrosion pits under the rust layer of high-manganese steel often exhibit a pronounced tendency to propagate in depth [22].
In summary, this chapter simulates polluted marine atmospheric environments by adding NaCl at mass fractions of 1% and 3.5% to 0.01-mol/L NaHSO3 solutions. Using water-toughened high-manganese steel as the research material, this study explores the stress corrosion behavior and mechanisms of high-manganese steel.
1.2 Early-stage Corrosion Initiation Behaviors of Composite Inclusions in High-manganese Steel
1.2.1 Materials and Methods
1.2.1.1 Materials and Solutions
The material used in this experiment was as-cast high-manganese steel provided by a certain steel plant, with its chemical composition shown in Table 1.1. The as-cast high-manganese steel was heated to 1,100 °C in the furnace, held at that temperature for 1 hour, and then rapidly water-cooled. After this water-quenching treatment, a single austenitic structure was obtained. In this experiment, solutions of 0.01-mol/L NaHSO3+3.5-wt.% NaCl and 0.01-mol/L NaHSO3+1-wt.% NaCl were used to simulate contaminated marine atmospheric environments (hereinafter referred to as 0.01+3.5 and 0.01+1, respectively) [35-38]. After preparing the solutions and allowing them to stand for a period of time, their pH values were measured, which stabilize at a range of 3.8-4.4.
Table 1.1 Chemical composition of high-manganese steel Mn13 (wt.%).
Fe Mn C Si S P Al Cr 84.5 13.5 1.17 0.57 0.01 0.03 0.02 0.031.2.1.2 Material Microstructure Characterization
Using Wire Electrical Discharge Machining (WEDM), a 10-mm × 10-mm × 3-mm specimen of high-manganese steel was cut. After encapsulation, the specimen was progressively polished with sandpaper up to a grit size of 2,000#, followed by the use of and oily polishing pastes. Prior to the characterization of the microstructure of the high-manganese steel, the polished working surface was etched with 4-vol.% nitric acid in alcohol for 5 seconds. After the surface turned black, it was rapidly rinsed with deionized water and alcohol, then blow-dried. The microstructure was observed using a stereo microscope. Electron backscatter diffraction (EBSD) technology was utilized to observe the crystallographic information of the high-manganese steel. X-ray diffraction (XRD) technology was employed to analyze the phase composition of the high-manganese steel, with a scanning rate of 6°/min and a diffraction angle scanning range of 10-90°.
1.2.1.3 Electrochemical Testing
The electrochemical testing employed in this chapter utilized a traditional three-electrode system. The specimen dimensions were 10 mm × 10 mm × 3 mm. Prior to the start of testing, a 30-minute open-circuit test was conducted to ensure the stability of the testing system, with the criterion for stability being that the sample potential fluctuation does not exceed 10 mV over a 5-minute period. The frequency range for the AC impedance test was from 100 kHz to 0.01 Hz, with an AC sine wave amplitude of 10 mV. For the potentiodynamic polarization curve test, the scanning rate was 0.5 mV/s, and the scanning range was from -0.3 to 0.4 V (vs. OCP [open-circuit potential]). The results were fitted using EC-Lab software.
1.2.1.4 Microcellular Surface Potential Measurements
In order to investigate the potential difference between inclusions and the matrix in high-manganese steel, tests were carried out using the scanning Kelvin probe force microscopy (SKPFM), a noncontact scanning system based on the development of electrochemical scanning probes, where potentials are formed due to the difference in the Fermi energy levels between the tip of the needle and the sample when the probe is sufficiently close to the surface of the specimen, and the potential difference can be calculated as a function of work of the probe and the surface of the sample [39, 40],
(1.1)where is the work function of the probe tip, is the work function of the specimen surface, and is the electron charge.
In this experiment, SKPFM tests were performed using a Bruker Nanoscope Multimode 8 atomic force system with a PFQNE-AL silicon nitride/silicon probe with a standard elasticity constant of 0.8 N/m and a resonance frequency of 300 kHz. The specimens were ground step by step up to 2,000# and polished prior to the test, and an atomic-force smart imaging mode was utilized. ScanAsyst-Air was used to obtain the surface potential difference at a speed of 0.4 Hz at room temperature and 45% relative humidity. After the test was completed, the data were processed using Nanoscope Analysis 1.8 software.
1.2.1.5 In-situ Immersion Test for Inclusions
The high-manganese steel was made into a block specimen of 10 mm × 10 mm × 5 mm, and the working surface was ground step by step with sandpaper up to 2,000# and polished. The immersion experiments were carried out in the 0.01+1 and 0.01+3.5 solutions, two kinds of polluted marine atmosphere simulation solutions, and the immersion times were 0, 5, 10, 30, and 60 minutes, respectively. After immersion, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses were used to observe inclusions morphology and elements, and EBSD analysis was used to observe the degree of aberration around the inclusions.
1.2.2 Physicochemical Properties and Localized Corrosion Behavior
1.2.2.1 Physicochemical Properties of High-manganese Steel
Figure 1.1 shows the microstructure morphology and XRD analysis of high-manganese steel, respectively. From the figure, it can be seen that the microstructure of high-manganese steel is equiaxed single-phase austenite with a grain size of approximately . Many black carbide particles can be observed under an optical microscope (OM).
Figure 1.1 Microstructural analysis of high-manganese steel: (a) EBSD-IPF map, (b) OM image
(Source: Chao Li et al. (2024) / Springer Nature), and (c) XRD pattern.
To investigate the corrosion electrochemical behaviors of high-manganese steel in two simulated polluted marine atmospheric solutions, AC electrochemical impedance spectroscopy (EIS) tests were conducted, as shown in Figure 1.2. The figure reveals that the capacitive arc shapes of high-manganese steel in both solutions are consistent, with the radius of the capacitive arc in the 0.01+1 solution being larger than that in the 0.01+3.5 solution. A characteristic peak appears in the Bode plot near 0.1-1 Hz in the phase angle, which corresponds to the anode dissolution effect [41-48]. The impedance spectra were fitted using the equivalent circuit model shown in Figure 1.2c, where represents the solution resistance, and are the double-layer capacitance on the electrode surface and the product resistance hindering the electrode process due to corrosion products, respectively, and and are the constant-phase element of the double layer on the steel surface and the charge transfer resistance,...
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