Machine learning assistance for electrochemical curve simulation of corrosion and its application

Gong, Xiaoyu; Dong, Chaofang; Xu, Jingjun; Wang, Li; Liu, Xiaogang

doi:10.1002/maco.201911224

Cited by 23 publications

(18 citation statements)

References 13 publications

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“…They trained machine learning models to recognize failures from a database of simulated EIS. Similar approaches have been applied for corrosion, − for ceramic actuators, and for batteries. − …”

Section: Discussionmentioning

confidence: 99%

Impedance Analysis of Electrochemical Systems

2022

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Interpretation of impedance spectroscopy data requires both a description of the chemistry and physics that govern the system and an assessment of the error structure of the measurement. The approach presented here includes use of graphical methods to guide model development, use of a measurement model analysis to assess the presence of stochastic and bias errors, and a systematic development of interpretation models in terms of the proposed reaction mechanism and physical description. Application to corrosion, batteries, and biological systems is discussed, and emerging trends in interpretation and implementation of impedance spectroscopy are presented.

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Section: Discussionmentioning

confidence: 99%

Impedance Analysis of Electrochemical Systems

2022

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“…In addition to the microhardness, wear resistance is also one of the mechanical properties critical to the applications of Mg alloys in practice . Wear volume loss of AZ31 Mg alloy substrate, laser cladding Al, and Al/Al 2 O 3 coatings are shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

Al₂O₃ eliminating defects in the Al coating by laser cladding to improve corrosion and wear resistance of Mg alloy

Sheng¹,

Yi²,

Tong-fang³

et al. 2019

Materials & Corrosion

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Although Al produces a solid metallurgical bonding with Mg alloy substrates, micropores or crevices in the Al coating can reduce the resistance of Mg alloy to corrosion. In this study, a composite coating with a defect-free microstructure was prepared on the AZ31 Mg alloy substrate by introducing Al 2 O 3 into the Al matrix via the method of laser cladding. On the one hand, Al 2 O 3 with thermal insulation had a low thermal expansion coefficient and was not very prone to voids during laser melting. On the other hand, Al 2 O 3 particles with a small size acted as the filler in the micropores or crevices. The Al/Al 2 O 3 coating exhibited a smaller current density (2.1 × 10 −6 A/cm 2 ) in comparison with those of bare substrate and Al coating (158.4 × 10 −6 and 3.1 × 10 −6 A/cm 2 , respectively), which was mainly ascribed to the pore-free microstructure and high resistance to corrosion of Al 2 O 3 phase. A favorable microhardness value of 95.3 HV was achieved for Al/Al 2 O 3 coating, approximately 1.8 times higher than that of Al coating (52.8 V), which was mainly ascribed to the dispersion hardening of Al 2 O 3 phase. Meanwhile, the Al/Al 2 O 3 coating significantly reduced wear volume from 2.8 mm 3 /m of Al coating to 0.4 mm 3 /m, showing great potential for weight reduction applications. K E Y W O R D S coating, corrosion, laser cladding, mechanical properties

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“…In recent years, many research studies have been conducted on the using of ML to predict the corrosion behavior of materials. Gong et al (2020) attempted to construct polarization curves and electrochemical impedance spectra (EIS) using k-nearest neighbor, decision tree and its ensemble methods, and support vector machine. They demonstrated that random forest (RF) had the highest accuracy in predicting the polarization curve and EIS.…”

Section: Introductionmentioning

confidence: 99%

Prediction of hot corrosion behavior of Inconel 617 via machine learning

Rezaei

2023

ACMM

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Purpose This paper aims to study the feasibility of using machine learning in hot corrosion prediction of Inconel 617 alloy. Design/methodology/approach By examination of the experimental studies on hot corrosion of Inconel 617, a data set was built for machine learning models. Apart from the alloy composition, this paper included the condition of hot corrosion like time and temperature, and the composition of the saline medium as independent features, while the specific mass change is set as the target feature. In this paper, linear regression, random forest and XGBoost are used to predict the specific mass gain of Inconel 617. Findings XGBoost yields the coefficient of determination (R2) of 0.98, which was highest among models. Also, this model recorded the lowest value of mean absolute error (0.20). XGBoost had the best performance in predicting specific mass gain of the alloy in different times at temperature of 900°C. In sum, XGBoost shows highest accuracy in predicting specific mass gain for Inconel 617. Originality/value Using machine learning to predict hot corrosion in Inconel 617 marks a substantial progress in this domain and holds promise for simplifying the development and evaluation of novel materials featuring enhanced hot corrosion resilience.

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Machine learning assistance for electrochemical curve simulation of corrosion and its application

Cited by 23 publications

References 13 publications

Impedance Analysis of Electrochemical Systems

Impedance Analysis of Electrochemical Systems

Al₂O₃ eliminating defects in the Al coating by laser cladding to improve corrosion and wear resistance of Mg alloy

Prediction of hot corrosion behavior of Inconel 617 via machine learning

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Machine learning assistance for electrochemical curve simulation of corrosion and its application

Cited by 23 publications

References 13 publications

Impedance Analysis of Electrochemical Systems

Impedance Analysis of Electrochemical Systems

Al2O3 eliminating defects in the Al coating by laser cladding to improve corrosion and wear resistance of Mg alloy

Prediction of hot corrosion behavior of Inconel 617 via machine learning

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Product

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Al₂O₃ eliminating defects in the Al coating by laser cladding to improve corrosion and wear resistance of Mg alloy