Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods
Abstract This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB2) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB2-CDA/Al6061 composites was systematically analyzed under...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01336-0 |
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| author | Amit Aherwar Anamika Ahirwar Vimal Kumar Pathak |
| author_facet | Amit Aherwar Anamika Ahirwar Vimal Kumar Pathak |
| author_sort | Amit Aherwar |
| collection | DOAJ |
| description | Abstract This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB2) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB2-CDA/Al6061 composites was systematically analyzed under dry sliding conditions utilizing a pin-on-disc setup. The study investigates the effects of key parameters, including reinforcement percentage (R), applied load (L), sliding velocity (V), and sliding distance (D), on wear loss and the coefficient of friction (COF) through a full factorial experimental design. Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. The models were trained and validated using experimental data, demonstrating the efficacy of machine learning in accurately predicting tribological performance while minimizing extensive experimental trials. Among the models, GPR exhibited the highest predictive accuracy, surpassing other algorithms in forecasting wear behaviour. |
| format | Article |
| id | doaj-art-ba9e8aa17db3493fa8cbccee4e9b7b05 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ba9e8aa17db3493fa8cbccee4e9b7b052025-08-20T02:32:08ZengNature PortfolioScientific Reports2045-23222025-05-0115112010.1038/s41598-025-01336-0Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methodsAmit Aherwar0Anamika Ahirwar1Vimal Kumar Pathak2Department of Mechanical Engineering, Madhav Institute of Technology and Science (Deemed University)Department of Computer Science and Engineering, Compucom Institute of Technology and ManagementDepartment of Mechanical Engineering, Manipal University JaipurAbstract This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB2) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB2-CDA/Al6061 composites was systematically analyzed under dry sliding conditions utilizing a pin-on-disc setup. The study investigates the effects of key parameters, including reinforcement percentage (R), applied load (L), sliding velocity (V), and sliding distance (D), on wear loss and the coefficient of friction (COF) through a full factorial experimental design. Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. The models were trained and validated using experimental data, demonstrating the efficacy of machine learning in accurately predicting tribological performance while minimizing extensive experimental trials. Among the models, GPR exhibited the highest predictive accuracy, surpassing other algorithms in forecasting wear behaviour.https://doi.org/10.1038/s41598-025-01336-0Al6061TiB2CowdungHybrid compositesWearMachine learning |
| spellingShingle | Amit Aherwar Anamika Ahirwar Vimal Kumar Pathak Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods Scientific Reports Al6061 TiB2 Cowdung Hybrid composites Wear Machine learning |
| title | Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods |
| title_full | Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods |
| title_fullStr | Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods |
| title_full_unstemmed | Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods |
| title_short | Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods |
| title_sort | dry sliding tribological characteristics evaluation and prediction of tib2 cda al6061 hybrid composites exercising machine learning methods |
| topic | Al6061 TiB2 Cowdung Hybrid composites Wear Machine learning |
| url | https://doi.org/10.1038/s41598-025-01336-0 |
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