Purpose -The purpose of this technical paper is to investigate the friction and wear behaviour of heat treated Al 6061 alloy and Al 6061 SiC-graphite particulate reinforced hybrid composites subjected to different ageing durations. Design/methodology/approach -The composites have been prepared by stir casting process with varying percentages of SiC and graphite particles. The cast 6061 alloy and its composites were subjected to solutionising treatment at a temperature of 803 K for 1 hr followed by quenching in water. The quenched samples were then subjected to artificial ageing for different durations of 4, 6, 8 hr at a temperature of 448 K. Tests were performed on heat treated Al 6061 alloy and its composites using pin-on-disc apparatus. Hardness measurements were also made on the specimens. The wear surfaces of the composites were analyzed using scanning electron microscopy. Findings -During wear test of specimens the wear resistance of the hybrid composites was found to increase with increase in ageing durations. The microscopic examination of the wear surfaces shows that the base alloy and composites wear primarily because of abrasion and delamination. The hardness result shows that the hardness of the composites increased with decreasing weight percentage of graphite particles. Originality/value -The content of this paper is fully research oriented and the finding from this investigation will be useful for society and also the automobile industries, especially in the making of brake drums.

Metal matrix composites, in particular, Aluminium Matrix Composites are gaining increasing attention for applications in aerospace, defence and automobile industries. The use of nonconventional machining techniques in shaping aluminum metal matrix composites has generated considerable interest as the manufacturing of complicated contours such as dies. Electrical discharge machining (EDM) appears to be a promising technique for machining metal matrix composites. The objective of this work is to investigate the effect of parameters like Current(I), Pulse on time(T), Voltage(V) and Flushing pressure(P) on metal removal rate (MRR),tool wear rate(TWR) as well as surface roughness(SR) on the machining of hybrid Al6061 metal matrix composites reinforced with 10% SiC and 4%graphite particles. Composite was fabricated using stir casting process. A central composite rotatable design was selected for conducting experiments. Mathematical models were developed using the MINITAB R14 software. The method of least squares technique was used to calculate the regression coefficients and Analysis of Variance (ANOVA) technique was used to check the significance of the models developed. Scanning Electron Microscope (SEM) analysis was done to study the surface characteristics of the machined specimens and correlated with the models developed.

Purpose -The purpose of this paper is to produce Al6061 metal matrix composites reinforced with silicon carbide (SiC) and graphite particulates and study their wear behavior and also to develop artificial neural network model to predict the mass loss of hybrid composites. Design/methodology/approach -The hybrid composites were produced by using stir casting process. The experiments were conducted based on the central composite rotatable design matrix using pin-on-disc wear testing machine. The set of data collected from the experimental values were used to train a back propagation (BP) learning algorithm with one hidden layer network. In artificial neural network (ANN) training module, four input vectors were used in the construction of proposed network namely, weight percentage of SiC particles, weight percentage of graphite particles, applied load and sliding distance. Mass loss was the output to be obtained from the proposed network. After training process, the test data collected from the experimental values were used to check the accuracy of proposed ANN model. Findings -The results show that the well trained one hidden layer network have smaller training errors and much better generalization performance and can be successfully used for the prediction of mass loss of hybrid aluminium metal matrix composites. Originality/value -In this paper the ANN method was adopted to predict the mass loss of hybrid composites. It was found that artificial neural network can be successfully used for prediction of mass loss of composites.

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Purpose – The purpose of this paper is to investigate the influence of most predominant heat-treatment parameters on the wear behavior of Al6061 hybrid composite reinforced with 10 weight per cent SiC and 2 weight per cent graphite particles. Design/methodology/approach – The aluminum hybrid composite was produced using stir casting process. Wear testing of heat-treated samples was carried out using a pin-on-disc apparatus. Experiments were conducted by applying design of experiments (DOE) technique. The experimental values were used for formulation of a mathematical model. The wear surfaces of composite specimens were analyzed using scanning electron microscope (SEM). Findings – The volume loss of heat-treated composite initially decreased with increasing aging duration. This was followed by the attainment of a minimum and then a reversal in the trend at longer aging times. SEM micrographs of the wear surfaces of the composite show that the wear mechanisms were abrasion, delamination and adhesion. Originality/value – In this paper, the hybrid composite was produced using stir casting route, and its wear properties after heat treatment were tested using pin-on-disc apparatus. It was found that heat treatment had a profound effect on the wear behaviour of the developed composite.