Sharath, Ballupete Nagaraju and Venkatesh, Channarayapattana Venkataramaiah and Afzal, Asif and Aslfattahi, Navid and Aabid, Abdul and Baig, Muneer and Saleh, Bahaa (2021) Multi ceramic particles inclusion in the aluminium matrix and wear characterization through experimental and response surface-artificial neural networks. Materials, 14 (11). ISSN EISSN 1996-1944, DOI https://doi.org/10.3390/ma14112895.
Full text not available from this repository.Abstract
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 B4C 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 B4C 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 B4C 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 B4C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B4C 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%.
| Item Type: | Article |
|---|---|
| Funders: | Structures and Materials (S&M) Research Lab of Prince, Prince Sultan University processing charges (APC) |
| Uncontrolled Keywords: | B4C; Gr; Al2219; Delamination wear; MML; MMCs; Artificial neural networks |
| Subjects: | Q Science > QC Physics Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) T Technology > TN Mining engineering. Metallurgy |
| Divisions: | Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 18 Aug 2022 02:48 |
| Last Modified: | 18 Aug 2022 02:48 |
| URI: | http://eprints.um.edu.my/id/eprint/28722 |
Actions (login required)
 |
View Item |
