| Autor: |
D. Dinesh Kumar, A. Balamurugan, K. C. Suresh, R. Suresh Kumar, N. Jayanthi, T. Ramakrishnan, S. K. Hasane Ahammad, S. Mayakannan, S. Venkatesa Prabhu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Chemistry, Vol 2023 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2090-9071 |
| DOI: |
10.1155/2023/2554098 |
| Popis: |
The effects of the percentage volume of reinforcement, the ratio of reinforcement, and the matrix size of particles on the wear behavior of AA5052/B4C metal matrix composites (MMCs) examine. This research examines a model function developed from an artificial neural network (ANN). AA5052/B4C composites bent using a powder metallurgy technique to hardness and ball-on-disc wear testing. There are two exemptions such as (1) when the percentage volume of reinforcement is less than 8% and (2) when the ratio of reinforcement particle size (Rs) and matrix particle size (Ms) increases before decreasing. The results show that wear loss decreases with increasing percentage volume of reinforcement and ratio of Rs and Ms. In the second case, wear loss is increased at high levels of percentage volume (14%) since the proportion of reinforcement and matrix size of the particle is close to 1. When the volume percentage of reinforcement is high (14%) and the matrix and reinforcement particle sizes are substantial (120 m), the reinforcement particles become dislodged and break. Because these broken-up particles are easily removed from the surface, the material’s wear resistance is reduced. In this case, raising the volume fraction yields a uniformly higher hardness for all Rs/Ms values; hence, composites with lower reinforcement volume percentages show better wear resistance. Hardness and wear resistance have no relationship with one another. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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