Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks
The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2022-05-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1486 |
| _version_ | 1797759516254666752 |
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| author | rusul khalid al hamad Nawal J. Hammadi |
| author_facet | rusul khalid al hamad Nawal J. Hammadi |
| author_sort | rusul khalid al hamad |
| collection | DOAJ |
| description |
The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.
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| first_indexed | 2024-03-12T18:45:31Z |
| format | Article |
| id | doaj.art-4253598d8fce4efcaf81140442784230 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-12T18:45:31Z |
| publishDate | 2022-05-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-4253598d8fce4efcaf811404427842302023-08-02T07:40:24ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392022-05-0128510.31026/j.eng.2022.05.02Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networksrusul khalid al hamad0Nawal J. Hammadi1Polymers And Petrochemical Engineering Dep. Basrah University For Oil And Gas, Iraq -BasrahCollege of Engineering – University of Basrah, Iraq –Basrah The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944. https://joe.uobaghdad.edu.iq/index.php/main/article/view/1486Pitting Corrosion, Carbon Steel , Pits Depth , Pit Density , ANNs. |
| spellingShingle | rusul khalid al hamad Nawal J. Hammadi Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks Journal of Engineering Pitting Corrosion, Carbon Steel , Pits Depth , Pit Density , ANNs. |
| title | Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks |
| title_full | Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks |
| title_fullStr | Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks |
| title_full_unstemmed | Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks |
| title_short | Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks |
| title_sort | estimating pitting corrosion depth and density on carbon steel c 4130 using artificial neural networks |
| topic | Pitting Corrosion, Carbon Steel , Pits Depth , Pit Density , ANNs. |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1486 |
| work_keys_str_mv | AT rusulkhalidalhamad estimatingpittingcorrosiondepthanddensityoncarbonsteelc4130usingartificialneuralnetworks AT nawaljhammadi estimatingpittingcorrosiondepthanddensityoncarbonsteelc4130usingartificialneuralnetworks |