Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms
Abstract The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | Friction |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40544-023-0847-2 |
| _version_ | 1797219567498428416 |
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| author | Huifeng Ning Faqiang Chen Yunfeng Su Hongbin Li Hengzhong Fan Junjie Song Yongsheng Zhang Litian Hu |
| author_facet | Huifeng Ning Faqiang Chen Yunfeng Su Hongbin Li Hengzhong Fan Junjie Song Yongsheng Zhang Litian Hu |
| author_sort | Huifeng Ning |
| collection | DOAJ |
| description | Abstract The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data. Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients, and the normal load, the content of graphite, and the hardness of the matrix influence the wear rates the most. |
| first_indexed | 2024-04-24T12:35:42Z |
| format | Article |
| id | doaj.art-039da442490e4833b15fa119ec1c2c2d |
| institution | Directory Open Access Journal |
| issn | 2223-7690 2223-7704 |
| language | English |
| last_indexed | 2024-04-24T12:35:42Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Friction |
| spelling | doaj.art-039da442490e4833b15fa119ec1c2c2d2024-04-07T11:30:21ZengSpringerOpenFriction2223-76902223-77042024-04-011261322134010.1007/s40544-023-0847-2Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithmsHuifeng Ning0Faqiang Chen1Yunfeng Su2Hongbin Li3Hengzhong Fan4Junjie Song5Yongsheng Zhang6Litian Hu7School of Electrical and Mechanical Engineering, Lanzhou University of TechnologySchool of Electrical and Mechanical Engineering, Lanzhou University of TechnologyState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesState Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of SciencesAbstract The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms. Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data. Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients, and the normal load, the content of graphite, and the hardness of the matrix influence the wear rates the most.https://doi.org/10.1007/s40544-023-0847-2self-lubricating compositesmachine learning (ML)tribological propertiesprediction |
| spellingShingle | Huifeng Ning Faqiang Chen Yunfeng Su Hongbin Li Hengzhong Fan Junjie Song Yongsheng Zhang Litian Hu Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms Friction self-lubricating composites machine learning (ML) tribological properties prediction |
| title | Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms |
| title_full | Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms |
| title_fullStr | Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms |
| title_full_unstemmed | Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms |
| title_short | Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms |
| title_sort | modeling and prediction of tribological properties of copper aluminum graphite self lubricating composites using machine learning algorithms |
| topic | self-lubricating composites machine learning (ML) tribological properties prediction |
| url | https://doi.org/10.1007/s40544-023-0847-2 |
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