Artificial neural network approach for the prediction of wear for Al6061 with reinforcements
In the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Materials Research Express |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2053-1591/aba0ec |
| _version_ | 1797746263357128704 |
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| author | Rahmath Ulla Baig Syed Javed Azharuddin Kazi Mohammed Quyam |
| author_facet | Rahmath Ulla Baig Syed Javed Azharuddin Kazi Mohammed Quyam |
| author_sort | Rahmath Ulla Baig |
| collection | DOAJ |
| description | In the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic particles are reinforced. In this endeavour, commercially available aluminium alloy is reinforced with 2, 4 and 6 wt% of silicon carbide (SiC) and Vanadium pentoxide (V _2 O _5 ) powder to improve its wear resistance. The intensity of reinforcement in the matrix was uniform, and the Scanning Electron Microscope image showed the grain refinement and grain boundary of the MMC’s. Wear tests were performed for L16 array set, uncertainty analysis of wear measurement is evaluated, and data were used to develop Artificial Neural Network (ANN) model. The efficient ANN model with a regression coefficient of 0.999 was used to make predictions for remaining sets. Experimental and predicted wear results were analysed; it is observed that higher wt% reinforcement of V _2 O _5 increased wear resistance of aluminium compared to SiC. The methodology adapted using ANN for prediction of wear using meagre experimentation, will lay a path for tribologists to predict the wear of novel metal matrix composites in their endeavour of finding wear-resistant materials. |
| first_indexed | 2024-03-12T15:35:21Z |
| format | Article |
| id | doaj.art-620100c56c404e53a33f334effdc61a7 |
| institution | Directory Open Access Journal |
| issn | 2053-1591 |
| language | English |
| last_indexed | 2024-03-12T15:35:21Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj.art-620100c56c404e53a33f334effdc61a72023-08-09T16:15:42ZengIOP PublishingMaterials Research Express2053-15912020-01-017707650310.1088/2053-1591/aba0ecArtificial neural network approach for the prediction of wear for Al6061 with reinforcementsRahmath Ulla Baig0Syed Javed1https://orcid.org/0000-0001-6035-3447Azharuddin Kazi2Mohammed Quyam3Department of Industrial Engineering, College of Engineering, King Khalid University , Abha, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, King Khalid University , Abha, Saudi ArabiaDepartment of Mechanical Engineering, PES University , South Campus, Bangalore, IndiaDepartment of Mechanical Engineering, PES University , South Campus, Bangalore, IndiaIn the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic particles are reinforced. In this endeavour, commercially available aluminium alloy is reinforced with 2, 4 and 6 wt% of silicon carbide (SiC) and Vanadium pentoxide (V _2 O _5 ) powder to improve its wear resistance. The intensity of reinforcement in the matrix was uniform, and the Scanning Electron Microscope image showed the grain refinement and grain boundary of the MMC’s. Wear tests were performed for L16 array set, uncertainty analysis of wear measurement is evaluated, and data were used to develop Artificial Neural Network (ANN) model. The efficient ANN model with a regression coefficient of 0.999 was used to make predictions for remaining sets. Experimental and predicted wear results were analysed; it is observed that higher wt% reinforcement of V _2 O _5 increased wear resistance of aluminium compared to SiC. The methodology adapted using ANN for prediction of wear using meagre experimentation, will lay a path for tribologists to predict the wear of novel metal matrix composites in their endeavour of finding wear-resistant materials.https://doi.org/10.1088/2053-1591/aba0ecArtificial Neural Networksilicon carbideVanadium pentoxideWearMetal Matrix CompositesANOVA |
| spellingShingle | Rahmath Ulla Baig Syed Javed Azharuddin Kazi Mohammed Quyam Artificial neural network approach for the prediction of wear for Al6061 with reinforcements Materials Research Express Artificial Neural Network silicon carbide Vanadium pentoxide Wear Metal Matrix Composites ANOVA |
| title | Artificial neural network approach for the prediction of wear for Al6061 with reinforcements |
| title_full | Artificial neural network approach for the prediction of wear for Al6061 with reinforcements |
| title_fullStr | Artificial neural network approach for the prediction of wear for Al6061 with reinforcements |
| title_full_unstemmed | Artificial neural network approach for the prediction of wear for Al6061 with reinforcements |
| title_short | Artificial neural network approach for the prediction of wear for Al6061 with reinforcements |
| title_sort | artificial neural network approach for the prediction of wear for al6061 with reinforcements |
| topic | Artificial Neural Network silicon carbide Vanadium pentoxide Wear Metal Matrix Composites ANOVA |
| url | https://doi.org/10.1088/2053-1591/aba0ec |
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