Application of hierarchical linear modelling to corrosion prediction in different atmospheric environments

Cai, Yikun; Zhao, Yu; Ma, Xiaobing; Zhou, Kun; Wang, Hao

doi:10.1080/1478422x.2019.1578067

Cited by 11 publications

(9 citation statements)

References 53 publications

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“…The value of n is affected by various environmental factors in different atmospheric types (rural, urban, industrial, and marine) (Fuente et al 2011). However, there is only limited works which have studied the relationship between n and the environmental factors and need to be further improvement (Cai et al 2019;Dean and Reiser 2002;Panchenko and Marshakov 2016;Panchenko et al 2014). The results show that the effects of the environmental factors are not or just partly statistically significant, which doesn't agree with the actual situation that the environment will inevitably lead to the difference of the composition and protection ability of the corrosion products.…”

Section: Summary Of the Current Literaturementioning

confidence: 90%

“…The influence of environmental factors on parameter n is more complicated and difficult to describe with quantitative models. Currently, only limited number of literatures have explored the relationship between parameter n and the environmental factors (Cai et al 2019;Dean and Reiser 2002;Panchenko et al 2014;Panchenko and Marshakov 2016). Dean and Reiser (2002) used a regression analysis method for the ISO CORRAG data to establish the relationship between exponent n and environmental variables including time-of-wetness (TOW), sulfur dioxide concentration (SO 2 ), and chloride deposition rate (Cl).…”

Section: Referencesmentioning

confidence: 99%

“…Although the form of the model is general, the prediction accuracy is not high due to the limited sample size and scatter of the data. Cai et al (2019) built a hierarchical linear model to predict the corrosion growth along with time in different environments of carbon steel, zinc, and copper of the ISO CORRAG program. The result shows that, statistically, only the exponent n of copper shows significant positive effects on sulfur dioxide.…”

Section: Referencesmentioning

confidence: 99%

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Atmospheric corrosion prediction: a review

Cai

Zhao

et al. 2020

Corrosion Reviews

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The atmospheric corrosion of metallic materials causes great economic loss every year worldwide. Thus, it is meaningful to predict the corrosion loss in different field environments. Generally, the corrosion prediction method includes three parts of work: the modelling of the corrosive environment, the calibration of the corrosion effects, and the establishment of the corrosion kinetics. This paper gives an overview of the existing methods as well as promising tools and technologies which can be used in corrosion prediction. The basic corrosion kinetic model is the power function model and it is accurate for short-term corrosion process. As for the long-term corrosion process, the general linear models are more appropriate as they consider the protective effect of the corrosion products. Most corrosion effect models correlate the environmental variables, which are characterized by the annual average value in most cases, with corrosion parameters by linear equations which is known as the dose-response function. Apart from these conventional methods, some mathematical and numerical methods are also appropriate for corrosion prediction. The corrosive environment can be described by statistical distributions, time-varying functions and even geographic information system (GIS), while the corrosion effect can be captured via response surface models and statistical learning methods.

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Section: Summary Of the Current Literaturementioning

confidence: 90%

Section: Referencesmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

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Atmospheric corrosion prediction: a review

Cai

Zhao

et al. 2020

Corrosion Reviews

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“…The authors proved that the developed NN model predicted corrosion rates that were consistent with the experimental sets. In a recent study, Cai et al [6] applied a hierarchical linear modeling technique to find the effect and growth of corrosion in Zinc, Carbon, and Copper metals under different conditions that resulted in a deviation of ± 30 to 50% over the experimental results for the studied metals. In this research, we focused on the prediction of corrosion and its accuracy against the measured experimental value using Bayesian Regularization-based Artificial Neural Network (BRANN) for CoCrMo alloy.…”

Section: Introductionmentioning

confidence: 99%

Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

Shaik¹,

Mantrala²,

Bakthavatchalam³

et al. 2021

J Bio Tribo Corros

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The well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

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“…In conventional regression analysis, linear function, polynomial function and power function are mostly used [1,15]. Because of the limitations in dealing with multilevel structured data, conventional regression analysis is usually limited in case studies of particular steel's corrosion behavior or the influence of individual factors [16]. Exploring the corrosion behavior in different environments is challenging because the corrosion process is influenced by multiple environmental factors simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Yan

Diao

Lang

et al. 2020

Science and Technology of Advanced Materials

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The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R 2 values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.

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Application of hierarchical linear modelling to corrosion prediction in different atmospheric environments

Cited by 11 publications

References 53 publications

Atmospheric corrosion prediction: a review

Atmospheric corrosion prediction: a review

Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

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