Repository of Research and Investigative Information

Repository of Research and Investigative Information

Bam University of Medical Sciences

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

(2022) Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models. Frontiers in Materials. ISSN 22968016 (ISSN)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate coating. This study described a new reliability simulation framework to determine the corrosion behavior of coating using a gene artificial neural network (ANN) to estimate the corrosion current of the coating. The input parameters of the model are temperature, pH of electroplating bath, current density, and Ni2+ concentration, and corrosion current defined as output. The effectiveness and accuracy of the model were checked by utilizing the absolute fraction of variance (R2 = 0.9999), mean absolute percentage error (MAPE = 0.0171), and root mean square error (RMSE= 0.0002). This is determined using the genetic algorithm (GA) and the optimum practice condition. Copyright © 2022 Zeraati, Abbasi, Ghaffarzadeh, Chauhan and Sargazi.

Item Type: Article
Keywords: artificial neural network corrosion current gene expression programming genetic algorithm modeling Zn–Ni electrophosphate coating Corrosion resistance Corrosion resistant coatings Corrosive effects Galvanizing Gene expression Mean square error Neural networks Steel corrosion Zinc coatings Zinc compounds Corrosion behaviour Galvanized steels Gene-expression programming Input parameter Programming models Reliability simulation Simulation framework Genetic algorithms
Divisions:
Journal or Publication Title: Frontiers in Materials
Journal Index: Scopus
Volume: 9
Identification Number: https://doi.org/10.3389/fmats.2022.823155
ISSN: 22968016 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.mubam.ac.ir/id/eprint/1450

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