Friction stir process parameters optimization in the fabrication of agate particle reinforced AA6061-T6 aluminum alloy surface composite
The main objective of this study was to optimize the friction stir process parameters to fabricate agate particle reinforced AA6061-T6 Aluminum alloy surface composite for hardness and tensile strength. The surface composite samples were made using a CNC milling machine based on Taguchi’s mixed L16...
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Veröffentlicht in: | Discover Applied Sciences 2024-09, Vol.6 (10), p.493 |
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Sprache: | eng |
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Zusammenfassung: | The main objective of this study was to optimize the friction stir process parameters to fabricate agate particle reinforced AA6061-T6 Aluminum alloy surface composite for hardness and tensile strength. The surface composite samples were made using a CNC milling machine based on Taguchi’s mixed L16 orthogonal array. The process parameters such as tool rotational speed, traverse speed, percent of reinforcement, number of passes and pin profiles were selected in this study. The optimal properties for the developed surface composite were obtained when surface composites having 18% reinforcement were fabricated by a two-pass FSP using a squared pin tool with a rotational speed of 1400 rpm and traverse speed of 60 mm/min. At the optimal process parameters, the tensile strength and hardness of the fabricated surface composite were found to be 312.23 MPa and 74.82 HRB respectively. ANOVA analysis performed at a 95% confidence level revealed that all process parameters were significant and the contribution of the number of passes on the properties of surface composite was higher than other process parameters. Microstructural analysis revealed that medium rotational speed and high traverse speed produced a uniform distribution of agate particles which helped to improve the properties.
Article Highlights
A new aluminum alloy surface composite produced with friction stir processing.
Studied the effects of process parameters on tensile strength and hardness of the surface composite.
Grey relational analysis (GRA) identified the best process parameters for optimum properties. |
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ISSN: | 2523-3963 3004-9261 2523-3971 |
DOI: | 10.1007/s42452-024-06210-8 |