Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

Pei, Zibo; Zhang, Dawei; Zhi, Yuanjie; Yang, Tao; Jin, Lulu; Fu, Dongmei; Cheng, Xuequn; Terryn, Herman; Mol, J.M.C.; Li, Yong

doi:10.1016/j.corsci.2020.108697

Cited by 114 publications

(41 citation statements)

References 65 publications

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“…Environment Atmospheric Factors Year [9] Indoor Atmosphere Wet-dry cycles 2014 [4] Indoor Atmosphere T, RH, and formic and acetic acid 2014 [10] Outdoor Atmosphere T, RH 2014 [11] Outdoor Automobile T, RH, and salt particles 2014 [12] Outdoor Automobile T, RH, salt spray, and freeze 2014 [15] Outdoor Atmosphere T, RH, SO 2 , NO 2 , PM2.5, and PM10 2018 [5] Outdoor Atmosphere T, RH, SO 2 , NO 2 , PM2.5, and PM10 2019 [13] Outdoor Atmosphere T, RH, Rainfall, SO 2 , NO 2 , PM2.5, PM10, O 3 , and CO 2020 [14] Outdoor Atmosphere T, RH, and rainfall 2021…”

Section: Papers Conditionmentioning

confidence: 99%

“…The obtained data suggested that 75% of zinc corroded in the salt spray phase [12]. The atmospheric corrosion of carbon steel has been Materials 2021, 14, 6954 2 of 13 monitored by ACM, and the impacts of relative humidity, temperature, and rainfall have been identified to be higher than pollution on initial atmospheric corrosion [13]. Exposure tests with ACM have found that rainfall precipitation is the strongest parameter influencing the initial atmospheric corrosion rate in rainy regions, with RH taking the first place in low-precipitation environments and non-rainfall periods [14].…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Meng

Zhang²,

Wang³

et al. 2021

Materials

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An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.

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Section: Papers Conditionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Meng

Zhang²,

Wang³

et al. 2021

Materials

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“…The residual lifetime of a pipeline has been estimated considering the corrosion's effect using mathematical modelling [23]. Fe/Cu type galvanometer sensor was used to predict the corrosion effect for 34 days, and the researchers could achieve better results through the support vector regression model [24]. Current prediction models for additive processed alloys are either complicated or built on a limited basis.…”

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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“…Corrosion causes significant deterioration of the overall properties and durability of metallic materials and is a major threat to structural safety, safety of life, environmental health and economic growth [1]. As metals come into contact with various environments, such as air, water, chemical compounds or pollutants, they tend to decay due to the interaction of metal with the environment [2].…”

Section: Introductionmentioning

confidence: 99%

Morphology and Corrosion Behavior of Zn-Ni Layers Electrodeposited on Low Alloy Carbon Steel Substrate

Chouia¹,

Chala²,

Abdelghani³

et al. 2021

ACSM

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The aim of this work is to improve the microstructure, the morphology, the mechanical and the corrosion behavior of Zn-Ni layers electrodeposited on low alloy carbon steel. Some factors such as the nickel concentration in the electrolyte, the electrolyte temperature and the current density are studied and optimized. The efficiency of the layers to protect the low alloy carbon steel against corrosion in HCl solution is also studied in this work. The electrodeposited Zn-Ni layers morphologies and microstructure were investigated by Scanning Electronic Microscopy (SEM) and X-ray diffraction (XRD) respectively. The best deposits of Zn-Ni layers are obtained with 50%-Ni in the electrolyte bath at a temperature of 40°C and a current density of j = 3 A dm-2. The XRD spectrum showed the coexistence of two phases: δ-phase (Ni3Zn22) and γ-phase (Ni5Zn21), and a pyramidal morphology is detected by SEM. The lost mass method results showed that the corrosion rate tacked a steady state between 10 and 50% of Ni, and it increased with the increase of Ni amount in the electrolyte bath.

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Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

Cited by 114 publications

References 65 publications

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

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

Morphology and Corrosion Behavior of Zn-Ni Layers Electrodeposited on Low Alloy Carbon Steel Substrate

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