Corrosion resistance of corten steel – A review

Raja, V.K. Bupesh; Palanikumar, K.; Renish, R. Rohith; Babu, A.N. Ganesh; Varma, Jashwanth; Gopal, P. M.

doi:10.1016/j.matpr.2021.01.334

Cited by 19 publications

(11 citation statements)

References 46 publications

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“…In order to mitigate potential biases and variance problems, we employed the K-Fold cross-validation technique, thereby bolstering the reliability of our model evaluation. The model's performance was evaluated using a set of metrics including MSE, RMSE, MAE, MAPE, and the R-Square [2][3][4][5][6][7][8][9], which that metrics are quantify the model's predictions accuracy. A lower value for these metrics indicates a higher level of precision in the model [20,21].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Herowati,

Aji Eko Prabowo,

Akrom

et al. 2024

KEG

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The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time. Keywords: machine learning, corrosion inhibition, pyrimidine, QSPR model, predictive analysis

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Section: Methodsmentioning

confidence: 99%

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Herowati,

Aji Eko Prabowo,

Akrom

et al. 2024

KEG

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“…e uniqueness of corten steel lies in the proper combination of alloying elements in it. Several investigators have observed that, if the quantity of elements like Mn increases, the corrosion rate will increase and also the structure of the rust formed will be poor [2,3]. Ni-based coatings through electroplating route has enhanced the wear and corrosion resistance of the steel substrate and mechanical properties, as discussed elsewhere [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 98%

Investigation on Wear and Corrosion Behavior of Cu, Zn, and Ni Coated Corten Steel

Kumaravel

Raja

Balthaser

et al. 2022

Advances in Materials Science and Engineering

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Corten steel is a low-carbon alloy steel. It is widely used in architecture, the transport sector, and industrial applications, where the steel is exposed to harsh environments. It is very much sought after due to its auto protection from corrosive environments through the formation of patina (rust). The specialty of patina formed on the corten steel is that it can self-heal itself and stop the spreading of corrosion. Generally, steels are given protective coatings to enhance resistance to corrosion, wear, abrasion, etc. One of the popular protective coating techniques is electroplating. In this study, the effect of electroplating of copper (Cu), zinc (Zn), and nickel (Ni) on the wear and corrosion behavior of Corten ASTM A242 grade steel is investigated. It was observed that the Cu coating yielded poor corrosion and wear protection performance. The Zn coating exhibited a moderate improvement. The Ni electroplating produced excellent results and, the wear and corrosion resistance was improved in the corten steel. Thus, when compared with Cu, Zn, and Ni coatings, the Ni-coated corten steel is an ideal candidate in applications where there is a need for good resistance to wear, abrasion, and corrosion.

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“…WS is a kind of low-alloy steel with a certain number of alloying elements added, of which the main alloy components are Cu, P, Cr, and Ni. Under atmospheric corrosion, the WS will form a protective dense rust layer on the steel surface, and it is well-combined with the substrate, which could improve the corrosion resistance of the material [4][5][6][7][8]. Despite that, the anticorrosion mechanism of such newtype steel is relatively ambiguous, which actually becomes a main obstacle for the widespread application of WS.…”

Section: Introductionmentioning

confidence: 99%

Corrosion resistance behavior of weathering GR65 steel under marine and industrial atmospheric environment

Liu,

Gao,

Wang

et al. 2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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This paper focused on the corrosion resistance of weathering GR65 steel under marine and industrial atmospheric environments, the weight loss method and Tafel method were respectively used to judge the anticorrosion mechanism. The corrosion resistance of Q345 steel, which has been commonly used in transmission towers and steel factory buildings, was also investigated for comparison. The 3.5%NaCl solution and 3.5%Na2SO4 solution were respectively adopted to simulate the marine and industrial atmospheric environment, and the corrosion resistance of GR65 and Q345 steel in these two types of solution were carefully discussed and compared. The results show that GR65 and Q345 are more prone to corrosion in 3.5%NaCl solution than in 3.5%Na2SO4 solution, Specifically, the corrosion rate of GR65 is about 51% and 52.7% lower than that of Q345 respectively in 3.5%NaCl and 3.5%Na2SO4 solution. The rust layer on the surface of Q345 steel is poor in continuity, loose-porous, and easy to peel, which fails to prevent the rust layer from continuing to develop inward. Comparatively, the rust layer of GR65 surface exhibits good density, continuity, and strong adhesion, which is helpful in slowing down the further development of the rust. Thus, the GR65 shows better corrosion resistance than Q345 steel.

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Corrosion resistance of corten steel – A review

Cited by 19 publications

References 46 publications

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Investigation on Wear and Corrosion Behavior of Cu, Zn, and Ni Coated Corten Steel

Corrosion resistance behavior of weathering GR65 steel under marine and industrial atmospheric environment

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