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Estimation of Tribological Performance of Al-MMC Reinforced with a Novel In-Situ Ternary Mixture using Grey Relational Analysis


Affiliations
1 Faculty of Mechanical Engineering, Gandhi Group of Institutions (GIET), Gunupur – 765022, Odisha, India
2 Faculty of Mechanical Engineering, RVR and JC, Guntur – 522019, Andhra Pradesh, India
 

Objectives: To prepare Aluminium Metal matrix Composite (AMC) reinforced with treated fly ash (TFA) and untreated fly ash (UFA) by stir casting route with percentage of volume ≈ 15%. Methods/Statistical Analysis: Fly ash particulates are treated in plasma reactor; Al-TFA composite has been compared with Al-UFA and Al-Si alloy. The experiments are designed based on response surface methodology (RSM). In this study, sliding time, sliding distance and load as input parameters whereas weight loss (g) and coefficient of friction (COF) are response. Moreover, multi-outputs are convinced into a single outcome by grey relational analysis (GRA) to avoid uncertainty in decision making. Findings: X-ray studies corroborate the presence of SiC in TFA. Mechanical (i.e. Hardness, Tensile and Impact strength) and Physical (i.e. density) properties of Al-TFA composite exhibited better than Al-UFA and Base matrix. From the ANOVA of TFA notify that load is the most affecting factor followed sliding time and combined effect of sliding velocity and load on grey relational grade (GRG). The obtained optimal condition is T1V1L1 (i.e. sliding time (1000see), velocity (2m/s) and Load (30N)). The optimum value of weight loss and coefficient of friction (COF) are 0.019 grams and 0.168 respectively. Finally, confirmation test is conducted to validate the regression equation and the worn-out surfaces are examined by Scanning Electron Microscopic (SEM). Application/Improvements: Al-TFA composite exhibited better mechanical and tribological properties than Al-UFA composite.

Keywords

Al-MMCs, ANOVA, Fly Ash, GRA, RSM.
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  • Estimation of Tribological Performance of Al-MMC Reinforced with a Novel In-Situ Ternary Mixture using Grey Relational Analysis

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Authors

Siva Sankara Raju
Faculty of Mechanical Engineering, Gandhi Group of Institutions (GIET), Gunupur – 765022, Odisha, India
Ajit Ku. Senapathi
Faculty of Mechanical Engineering, Gandhi Group of Institutions (GIET), Gunupur – 765022, Odisha, India
Gunji Srinivas Rao
Faculty of Mechanical Engineering, RVR and JC, Guntur – 522019, Andhra Pradesh, India

Abstract


Objectives: To prepare Aluminium Metal matrix Composite (AMC) reinforced with treated fly ash (TFA) and untreated fly ash (UFA) by stir casting route with percentage of volume ≈ 15%. Methods/Statistical Analysis: Fly ash particulates are treated in plasma reactor; Al-TFA composite has been compared with Al-UFA and Al-Si alloy. The experiments are designed based on response surface methodology (RSM). In this study, sliding time, sliding distance and load as input parameters whereas weight loss (g) and coefficient of friction (COF) are response. Moreover, multi-outputs are convinced into a single outcome by grey relational analysis (GRA) to avoid uncertainty in decision making. Findings: X-ray studies corroborate the presence of SiC in TFA. Mechanical (i.e. Hardness, Tensile and Impact strength) and Physical (i.e. density) properties of Al-TFA composite exhibited better than Al-UFA and Base matrix. From the ANOVA of TFA notify that load is the most affecting factor followed sliding time and combined effect of sliding velocity and load on grey relational grade (GRG). The obtained optimal condition is T1V1L1 (i.e. sliding time (1000see), velocity (2m/s) and Load (30N)). The optimum value of weight loss and coefficient of friction (COF) are 0.019 grams and 0.168 respectively. Finally, confirmation test is conducted to validate the regression equation and the worn-out surfaces are examined by Scanning Electron Microscopic (SEM). Application/Improvements: Al-TFA composite exhibited better mechanical and tribological properties than Al-UFA composite.

Keywords


Al-MMCs, ANOVA, Fly Ash, GRA, RSM.



DOI: https://doi.org/10.17485/ijst%2F2017%2Fv10i15%2F151481