Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks
Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B<sub>4</sub>C in manufacturing AMMCs through st...
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2021-05-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/14/11/2895 |
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| author | Ballupete Nagaraju Sharath Channarayapattana Venkataramaiah Venkatesh Asif Afzal Navid Aslfattahi Abdul Aabid Muneer Baig Bahaa Saleh |
| author_facet | Ballupete Nagaraju Sharath Channarayapattana Venkataramaiah Venkatesh Asif Afzal Navid Aslfattahi Abdul Aabid Muneer Baig Bahaa Saleh |
| author_sort | Ballupete Nagaraju Sharath |
| collection | DOAJ |
| description | Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B<sub>4</sub>C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B<sub>4</sub>C and Gr particles led to continuous improvements in wear resistance. The microstructure and worn surface were observed through SEM (Scanning electron microscope) and revealed the formation of mechanically mixed layers of both B<sub>4</sub>C and Gr, which served as the effective insulation surface and protected the test sample surface from the steel disc. With the rise in the content of B<sub>4</sub>C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B<sub>4</sub>C and 10 wt.% Gr. A response surface analysis for the weight loss was carried out to obtain the optimal objective function. Artificial neural network methodology was adopted to identify the significance of the experimental results and the importance of the wear parameters. The error between the experimental and ANN results was found to be within 1%. |
| first_indexed | 2024-03-10T10:56:27Z |
| format | Article |
| id | doaj.art-443f1f62c73c42f7a3777907fecf121e |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T10:56:27Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-443f1f62c73c42f7a3777907fecf121e2023-11-21T21:47:56ZengMDPI AGMaterials1996-19442021-05-011411289510.3390/ma14112895Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural NetworksBallupete Nagaraju Sharath0Channarayapattana Venkataramaiah Venkatesh1Asif Afzal2Navid Aslfattahi3Abdul Aabid4Muneer Baig5Bahaa Saleh6Department of Mechanical Engineering, Malnad College of Engineering, Hassan, Affiliated to Visvesvaraya Technological University, Belagavi 573201, IndiaDepartment of Mechanical Engineering, Malnad College of Engineering, Hassan, Affiliated to Visvesvaraya Technological University, Belagavi 573201, IndiaDepartment of Mechanical Engineering, P. A. College of Engineering, Affiliated to Visvesvaraya Technological University, Belagavi, Mangaluru 574153, IndiaDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaEngineering Management Department, College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi ArabiaEngineering Management Department, College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi ArabiaMechanical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaLightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B<sub>4</sub>C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B<sub>4</sub>C and Gr particles led to continuous improvements in wear resistance. The microstructure and worn surface were observed through SEM (Scanning electron microscope) and revealed the formation of mechanically mixed layers of both B<sub>4</sub>C and Gr, which served as the effective insulation surface and protected the test sample surface from the steel disc. With the rise in the content of B<sub>4</sub>C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B<sub>4</sub>C and 10 wt.% Gr. A response surface analysis for the weight loss was carried out to obtain the optimal objective function. Artificial neural network methodology was adopted to identify the significance of the experimental results and the importance of the wear parameters. The error between the experimental and ANN results was found to be within 1%.https://www.mdpi.com/1996-1944/14/11/2895B<sub>4</sub>CGrAl2219delamination wearMMLMMCs |
| spellingShingle | Ballupete Nagaraju Sharath Channarayapattana Venkataramaiah Venkatesh Asif Afzal Navid Aslfattahi Abdul Aabid Muneer Baig Bahaa Saleh Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks Materials B<sub>4</sub>C Gr Al2219 delamination wear MML MMCs |
| title | Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks |
| title_full | Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks |
| title_fullStr | Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks |
| title_full_unstemmed | Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks |
| title_short | Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks |
| title_sort | multi ceramic particles inclusion in the aluminium matrix and wear characterization through experimental and response surface artificial neural networks |
| topic | B<sub>4</sub>C Gr Al2219 delamination wear MML MMCs |
| url | https://www.mdpi.com/1996-1944/14/11/2895 |
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