Prediction of Mg Alloy Corrosion Based on Machine Learning Models
Magnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its bone-like elastic modulus, biocompatibility, and lightweight properties. However, the rapid c...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9597155 |
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| _version_ | 1832565524603600896 |
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| author | Zhenxin Lu Shujing Si Keying He Yang Ren Shuo Li Shuman Zhang Yi Fu Qi Jia Heng Bo Jiang Haiying Song Mailing Hao |
| author_facet | Zhenxin Lu Shujing Si Keying He Yang Ren Shuo Li Shuman Zhang Yi Fu Qi Jia Heng Bo Jiang Haiying Song Mailing Hao |
| author_sort | Zhenxin Lu |
| collection | DOAJ |
| description | Magnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its bone-like elastic modulus, biocompatibility, and lightweight properties. However, the rapid corrosion rates of magnesium alloys seriously limit their applications. This study collected magnesium alloys’ corrosion data and developed a model to predict the corrosion potential, based on the chemical composition of magnesium alloys. We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector machine regression (SVR), and extreme gradient boosting (XGBoost). The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. Moreover, we used feature creation (transforming chemical component characteristics into atomic and physical characteristics) so that the input characteristics were not limited to specific chemical compositions. From this result, the model’s application range was widened, and machine learning was used to verify the accuracy and feasibility of predicting corrosion of magnesium alloys. |
| format | Article |
| id | doaj-art-ab37b8a25ef94addb8d96f30ced241a2 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-ab37b8a25ef94addb8d96f30ced241a22025-02-03T01:07:23ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/9597155Prediction of Mg Alloy Corrosion Based on Machine Learning ModelsZhenxin Lu0Shujing Si1Keying He2Yang Ren3Shuo Li4Shuman Zhang5Yi Fu6Qi Jia7Heng Bo Jiang8Haiying Song9Mailing Hao10School of StomatologySchool of StomatologySchool of StomatologySchool of StomatologySchool of StomatologySchool of StomatologySchool of StomatologyThe Conversationalist ClubThe Conversationalist ClubSchool of StomatologySchool of StomatologyMagnesium alloy is a potential biodegradable metallic material characterized by bone-like elastic modulus, which has great application prospects in medical, automotive, and aerospace industries owing to its bone-like elastic modulus, biocompatibility, and lightweight properties. However, the rapid corrosion rates of magnesium alloys seriously limit their applications. This study collected magnesium alloys’ corrosion data and developed a model to predict the corrosion potential, based on the chemical composition of magnesium alloys. We compared four machine learning algorithms: random forest (RF), multiple linear regression (MLR), support vector machine regression (SVR), and extreme gradient boosting (XGBoost). The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. Moreover, we used feature creation (transforming chemical component characteristics into atomic and physical characteristics) so that the input characteristics were not limited to specific chemical compositions. From this result, the model’s application range was widened, and machine learning was used to verify the accuracy and feasibility of predicting corrosion of magnesium alloys.http://dx.doi.org/10.1155/2022/9597155 |
| spellingShingle | Zhenxin Lu Shujing Si Keying He Yang Ren Shuo Li Shuman Zhang Yi Fu Qi Jia Heng Bo Jiang Haiying Song Mailing Hao Prediction of Mg Alloy Corrosion Based on Machine Learning Models Advances in Materials Science and Engineering |
| title | Prediction of Mg Alloy Corrosion Based on Machine Learning Models |
| title_full | Prediction of Mg Alloy Corrosion Based on Machine Learning Models |
| title_fullStr | Prediction of Mg Alloy Corrosion Based on Machine Learning Models |
| title_full_unstemmed | Prediction of Mg Alloy Corrosion Based on Machine Learning Models |
| title_short | Prediction of Mg Alloy Corrosion Based on Machine Learning Models |
| title_sort | prediction of mg alloy corrosion based on machine learning models |
| url | http://dx.doi.org/10.1155/2022/9597155 |
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