Characterisation of Silicon Carbide and Fly Ash in LM13 Aluminium Alloy Matrix Composites

Author(s):  
C. Krishnaraj ◽  
P. Divinesh ◽  
O.M. Mohaideen

The modern vehicles demand more thermal and mechanical properties as the speed of the vehicles is increasing. The materials used should be able to not only withstand the high temperatures but to dissipate it at a faster rate without deformation. This paper investigates the characteristics of silicon carbide (SiC) and fly ash in LM13 aluminium alloy matrix composite prepared by stir casting. The LM13 alloy has high thermal property which makes it ideal for making engines and gears. The effect of fly ash and SiC on LM13 and its influence on increasing the surface roughness was analyzed by varying their proportion. The addition of SiC and fly ash to the matrix composite increases the hardness and tensile strength of the composite which is validated by experimental results.

2021 ◽  
Vol 53 (4) ◽  
pp. 210412
Author(s):  
Priyadarsini Morampudi ◽  
Venkata Ramana V.S.N. ◽  
Koona Bhavani ◽  
Amrita M ◽  
V Srinivas

Aluminum matrix composites (AMCs) are crucial to the progress of composite application areas due to their remarkable mechanical properties. Their usage has expanded into different fields such as the aerospace, automobile, and defense industries. The present study used wrought Al alloy AA6061 as the matrix, while ilmenite (FeTiO3) particles were used as reinforcement at different weight percentages to prepare metal matrix composites. One of the most economical and simple casting routes among the several available fabrication techniques for the preparation of composites is the stir casting method, which was applied in the present investigation to prepare the AMCs. The machinability of the fabricated composites and the surface roughness property after machining were studied to understand the effect of speed and feed during machining. The results showed that an increase in speed decreased the cutting forces and the surface roughness. Meanwhile, an increase in surface roughness was observed with an increase in feed.


2017 ◽  
Vol 25 (3) ◽  
pp. 209-214 ◽  
Author(s):  
G. Venkatachalam ◽  
A. Kumaravel

This paper presents the characterization of A356 composite reinforced with fly ash and basalt ash produced by stir casting method. Aluminium metal matrix composites (AMC) are used in wide variety of applications such as structural, aerospace, marine, automotive etc. Stir casting is cost effective manufacturing process and it is useful to enhance the attractive properties of AMCs. Three sets of hybrid AMC are prepared by varying the weight fraction of the reinforcements (3% basalt + 7% fly ash, 5% basalt + 5% fly, 7% basalt + 3% fly ash). The effect of reinforcements on the mechanical properties of the hybrid composites such as hardness, tensile, compressive and impact strength were studied. The obtained results reveal that tensile, compressive and impact strength was increased when weight fraction of fly ash increased, whereas the hardness increases when weight fraction of the basalt ash increased. Microscopic study reveals the dispersion of the reinforcements in the matrix.


Author(s):  
Srinivasa Prasad Katrenipadu ◽  
Swami Naidu Gurugubelli

Nano-fly ash particles reinforced Al-10wt%Mg alloy matrix composites produced by stir-casting method were tested for their ageing response. Ageing studies were performed at 160 °C, 200 °C and 240 °C temperatures and a maximum peak hardness of 142 VHN was observed during ageing at 200 °C for the composite with 10 wt% nano fly ash reinforcement. This is due to rapid nucleation and growth of βI particles at this temperature. Experiments were designed for different compositions and different ageing temperatures on the basis of the Design of Experiments technique. The factorial design is considered to improve the reliability of results and to reduce the size of experimentation without loss of accuracy. A model to predict the ageing behaviour of the composites was developed with the terms of 5, 10 and 15% weight fraction of fly ash at 160 °C, 200 °C and 240 °C ageing temperatures. The developed regression model was validated by statistical software MINITAB-R17.1.0. It was found that the developed regression model could be effectively used to predict the ageing behavior at 95% confidence level.


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