Abstract As the critical first step in structural health management and fault diagnosis, corrosion monitoring is inherently multidisciplinary in nature. While conventional in situ techniques capture real-time electrical, vibrational, and thermal signatures, their effectiveness is constrained by limited detection precision, inefficient data analysis, and unreliable predictive modeling. The convergence of artificial intelligence (AI) and big data analytics has fundamentally transformed this field, generating considerable academic interest over the past decade. Machine learning (ML) – serving as the cornerstone of this revolution – excels not only in extracting nonlinear features from nonstationary processes but also employs probabilistic inference frameworks to quantify predictive uncertainty, thereby substantially augmenting in situ monitoring capabilities. This review systematically examines advancements in ML-assisted corrosion monitoring throughout the preceding decade, categorizing prevalent algorithms according to domain-specific implementations while evaluating enhanced in situ techniques through empirical case studies demonstrating superior data processing efficacy. Finally, we project future trajectories for intelligent monitoring technology in light of persistent challenges and emergent innovations.

Abstract Traditional neutral salt spray tests fail to accurately simulate the accelerated corrosion that occurs under actual energized conditions. We have developed a salt spray test chamber capable of reducing the relative humidity (RH) in the testing environment to prevent short circuits, insulation failures, and other faults that lead to premature damage during testing. Initially, the effect of salt spray concentration on corrosion at 85 % RH was examined, and it was found that the corrosion rate does not increase when the concentration surpasses 50 mg/m3. The humidity-controllable salt spray test demonstrated a closer match to real environmental corrosion, judging by both the appearance of corrosion and the acceleration ratios across different metals, where the acceleration ratio difference between maximum and minimum values was reduced from 18.6 times to 1.4 times. The influence of RH on the corrosion rate was revealed, with the highest rate observed at 75 % RH. Lastly, we proposed optimal parameters for energized salt spray tests, evaluated the failure modes of several products under these tests, preliminarily confirmed the consistency and acceleration effects of this method for electrical products in real marine atmospheric conditions.

Abstract Due to its lightweight structure, magnesium alloys have been widely used in the automotive and aerospace industries. However, the corrosion resistance of magnesium alloy is a key factor limiting their further application. Rare earth alloying is an economical and effective method to improve the corrosion resistance of magnesium alloys. In this work, Mg–2Y–2Al (wt%) alloy was prepared by conventional casting method. After 168 h of immersion in 3.5 wt% NaCl solution, the corrosion rates determined by hydrogen evolution test and mass loss experiment were 0.57 mm/y and 1.01 mm/y, respectively. The results provide a basic understanding of the synergistic effect of Al and Y on enhancing the corrosion resistance of magnesium alloys. The co-addition of Al and Y is conducive to the formation of a stronger protective film on the alloy surface and effectively inhibits the corrosion of alloys. It provides important insights for the future development of high-performance and corrosion-resistant magnesium alloys.

Abstract Atmospheric chloride deposition rate is the important factor in atmospheric corrosion. However, current research on the distribution of atmospheric chloride deposition in coastal region of China is limited, hindering accurate quantitative analysis of its corrosion effect. We conducted environment monitoring, deposition rate measurement, and metals exposure experiments in coastal region of Hainan Province, China. By analyzing the holistic process of atmospheric chloride, we proposed a deposition rate estimation model considering production, transportation, and deposition (PTD) processes. The proposed PTD regression model significantly improves estimation accuracy and generalizability, achieving an R 2 of 0.88 on the measured dataset. Additionally, based on environmental exposure experiments, we developed a metal corrosion loss assessment model for coastal region, well-validated on the four metals tested, with all R 2 values exceeding 0.92. Using these models, we constructed spatial and temporal distribution maps of atmospheric chloride deposition rate and its corrosion effects in coastal region of Hainan Province, China, providing guidance for the corrosion assessment, protection, and maintenance of metal products.

Abstract Microbiologically influenced corrosion (MIC) caused by sulphate-reducing bacteria (SRB) is an important issue, particularly in the offshore industry where cathodic protection (CP) is commonly used to prevent the corrosion of steel structures. However, its relation with MIC remains unclarified, as the interaction between CP and microbial activity is still far from understood. Under overprotection conditions, the water reduction reaction can lead to atomic hydrogen uptake, which may result in hydrogen embrittlement (HE). This uptake is influenced by calcareous deposits, i.e. inorganic compounds formed during CP. Additionally, SRB are argued in literature to enhance hydrogen uptake. This review explores various perspectives on the interaction between microbial activity, particularly SRB, and CP, as well as their individual and combined impact on hydrogen uptake in an offshore context.

Abstract Sulfide often appears in circulating cooling water due to the presence of sulfate reducing bacteria and could affect corrosion behavior of cooling pipe metals such as stainless steel. Scanning Kelvin probe and scanning electrochemical microscope measurements, combined with electrochemical testing, were used to investigate the micro-electrochemical information of passive film and analyzed the influence of sulfide in simulated cooling water on corrosion resistance of stainless steel. Results showed that the presence of sulfide in water caused a negative shift in surface potential of stainless steel, an increase in surface potential difference, and an increase in local response current on the surface, resulting in a current peak that gradually increased over time. The analysis results of passive film composition showed that the presence of sulfide caused increase in the ratio of Fe/Cr and OH − /O 2− , as well as the content of Cr(OH) 3 and Fe(OH) 3 in passive film, whereas caused a decrease of Cr 2 O 3 content, and led to the formation of FeS 2 in the passive film. These changes in the composition of the passive film made it easier for active sites to appear on the surface of stainless steel and enhanced the conductivity of the passive film and significantly reducing its protective performance.

Abstract This article describes the relationship that relates the static threshold parameter of K 1SCC (at ∼10−10 m/s) to ΔK th and K max,th (at ∼10−10 m/cycle) under fatigue load at high R-ratio like R = 0.85. At these high R-ratios (>0.75), applied K max > K 1SCC and the fracture path has a larger component of static fracture. 5083 Al alloy is adapted as an example to demonstrate this relationship at room temperature their relationship with and without GB-β anodic precipitates in inert and a 3.5 % NaCl solution environments. We are interested in the mechanical cyclic ΔK th and K max,th and quasi-static K ISCC driving forces affecting the damage in the presence of a chemical environment due to anodic dissolution of the GB-β precipitates. Detailed discussions involve how these parameters are interrelated.

Abstract Corrosion exposure study was conducted on the commercial nickel-based K411 superalloy in a simulated gas turbine operating environment (air + 2 vol% SO 2 ) at 900 °C up to 2000 h. The corrosion behavior of the alloy was quantificationally analyzed from both morphological and chemical points with SEM, XRD, EDS, and EPMA. The results show that the formation of fine TiN inside the oxide layer can be strongly accelerated with the introduction of SO 2 . Sulfide is assumed as diffusion channels for gas molecules that accelerate internal nitridation. Large-volume variation caused by the TiN formation leads to a stress gradient, which induces Cr and Ni elements from inside to the surface of the alloy. The oxide scales release the compressive stresses generated by internal nitridation through forming protrusions on the surface, which is a potential risk for alloy failure. The corrosion behavior of K411 superalloy is controlled by a combination of oxidation, sulfidation, and internal nitridation whereby the relevant corrosion mechanism has been given.

Abstract It is common to observe that monotonic K1c (lab air) and K1scc (NaCl solution) decrease with increasing YS of an alloy. K1c is measured in lab air and K1scc in an aqueous solution such as NaCl. It is noted that K1c is not considered as a subcritical parameter while K1scc is. Interestingly, for a given alloy, both these parameters seem to be inter-related. That is, K1scc is linearly related to K1c, such that K1scc increases with increasing K1c. This may indicate that plasticity is affecting the K1scc behavior. This article looks into the conditions that affect K1scc for steels, Al-alloys, and Ti-alloys. This linear variation of K1scc with K1c seems to be independent of alloy YS, E, microstructure, and work hardening rate. This observation seems similar in all three systems of alloys. How these parameters are interrelated is analyzed and discussed in detail.

Abstract To address the need for reduced vehicle weight and improved environmental sustainability, the automotive industry has increasingly turned to mixing lightweight materials and alloys with metal alloys. However, this integration of dissimilar materials has heightened the risk of galvanic corrosion. This study addresses the gap in modeling of galvanic corrosion under dynamic thin film electrolyte by incorporating data derived from real-world weather conditions and finite element simulations. The presented model successfully captures the trend of galvanic corrosion rate for a given atmospheric environmental condition. The model predictions are compared with experimental data in the literature. Good agreements are observed. The model is further used for prediction of galvanic corrosion of two identical vehicles located in two different geographic locations (i.e., Miami Beach in Florida and Wendover in Nevada) in the year 2021 leveraging weather station data. Additionally, a Bayesian estimation method is used to account for uncertainties in the model parameters and estimation of the probability of failure.