Data mining to online galvanic current of zinc/copper Internet atmospheric corrosion monitor

Shi, Yanan; Fu, Dongmei; Zhou, Xingyu; Yang, Tao; Zhi, Yuanjie; Pei, Zibo; Zhang, Dawei; Shao, Lei

doi:10.1016/j.corsci.2018.02.005

Cited by 40 publications

(25 citation statements)

References 20 publications

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“…In recent years, with the development of machine-learning algorithms, many studies have used machine-learning technology to establish the corrosion model and to implement the prediction of the corrosion status [9][10][11][12][13][14][15][16][17]. For example, Kamrunnahar [9,10], Jiang [11], Shirazi [12], and Shi [13] used artificial neural networks (abbreviated as ANN) to build the corrosion behavior model [9,10] or prediction model [11][12][13] of one specific alloy material.…”

Section: Introductionmentioning

confidence: 99%

“…Panchenko studied the law of corrosion as a function of the exposure time, and according to the mining law, he utilized the power function [15] and power-linear [16] function to implement the long-term prediction of corrosion. Shi analyzed and built a prediction model of the corrosion density data using a hidden Markov chain method [17]. However, in most of the above works, the objective was just one single material [9][10][11][12][13][14]17] or had one single input variable [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Shi analyzed and built a prediction model of the corrosion density data using a hidden Markov chain method [17]. However, in most of the above works, the objective was just one single material [9][10][11][12][13][14]17] or had one single input variable [15,16]. When extended to the corrosion prediction of various materials in a variety of environmental conditions, there still exist some problems in the above methods, leading to poor performance on the datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

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The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

Self Cite

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“…Some successful examples of the informatics-driven design of new materials include high-temperature alloys, low thermal hysteresis shape memory alloys, and metal additive manufacturing [ 19 – 21 ]. In the field of corrosion research, applications of machine learning methods such as support vector regression and artificial neural network were also reported [ 22 , 23 ]. These studies demonstrate the advantages of machine learning in correlation analysis, multivariate fitting, simulation, and data visualization [ 24 – 26 ].…”

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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“…Nonetheless, these methods are not suitable for long-term continuous testing on site, due to their high requirements in terms of equipment. In comparison, ACM is a simple device that can monitor the galvanic corrosion rate of metals in the atmosphere in real time [16,17,18]. An ACM is structured with an open galvanic cell in case of wetting; the current follows through the cell and the anode metal is corroded.…”

Section: Introductionmentioning

confidence: 99%

Corrosion Measurement of the Atmospheric Environment Using Galvanic Cell Sensors

Yang

Mei

Wang

2019

Sensors

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An atmospheric corrosion monitor (ACM) is an instrument used to track the corrosion status of materials. In this paper, a galvanic cell sensor with a simple structure, flexible parameters, and low cost was proposed for constructing a novel ACM, which consisted of three layers: the upper layer was gold, used as the cathode; the lower layer was corroded metal, used as the anode; and the middle layer was epoxy resin, used to separate the cathode and anode. Typically, the anode and epoxy resin were hollowed out, and the hollow parts were filled with electrolyte when it was wet to form a corrosive galvanic cell. Specifically, the corrosion rate was obtained by measuring the short circuit current of the cell. The sensor was made of a printed circuit board (PCB) or flexible printed circuit (FPC) and a metal coupon, which allowed for early control of the electrical parameters (including sensitivity and capacity) and could be combined with various metals. Additionally, the sensor feasibility was studied in water droplet experiments, during which the corrosive current changed with the electrolyte evaporation. The sensor practicability was also verified in a salt spray test, and the electric charge was compared using the thickness loss of bare coupons. A contrast test was also conducted for the corrosivity of different sensors made of aluminum, iron and copper.

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Data mining to online galvanic current of zinc/copper Internet atmospheric corrosion monitor

Cited by 40 publications

References 20 publications

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

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

Corrosion Measurement of the Atmospheric Environment Using Galvanic Cell Sensors

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