In this study, the fine and coarse aggregates were completely replaced by fly ash aggregates in fly ash concrete. A mix design was done for M20 grade of concrete by IS method. Ordinary Portland cement of 43 grade was selected and fly ash aggregates were prepared by mixing fly ash with cement and water. The properties of fly ash fine aggregates and fly ash coarse aggregates were studied. The aggregate crushing value and aggregate impact value of fly ash coarse aggregates were also studied. The cement and fly ash proportions of 10:90, 12.5:87.5, 15:85, 17.5:82.5, 20:80 and 22.5:77.5 were tried with a suitable water cement ratio 0.3 to get the fly ash aggregates. The concrete cubes, cylinders and beams were cast with the fly ash aggregates obtained from the above six cement fly ash proportions. Then the compressive strength, split tensile strength and flexural strength were tested and compared with control concrete. This paper briefly presents the compressive strength development of fly ash aggregate concrete at different ages. The split tensile strength and flexural strength of all the concrete mixes were also investigated at different days of curing.

Glass Fiber Reinforced Plastics composites have an increased application in recent days, due to its enhanced structural properties, Mechanical and thermal properties. Drilling of holes in GFRP becomes almost unavoidable in fabrication. The heterogeneous nature of this kind of materials makes complications during machining operation. However, drilling is a common machining practice for assembly of components. The quality of holes produced in the GFRP material is severely affected by surface roughness, Circularity, Delamination, etc. The objective of the study is to apply the full factorial design and Fuzzy logic model to achieve an improved hole quality considering the minimum Circularity error through proper selection of drilling parameters. The experimental investigation values are compared with predicted fuzzified values and found that they are in good correlation with each other.

Wide spread applications of composite materials have been significantly growing in aerospace, naval, space, and automotive industries. Drilling of such materials is a challenging task because of differential machining properties and checking the quality of hole is significantly a great attention. In this paper, experimental investigation on prediction of hole quality characteristics of aluminum matrix composite (AMC225xe) during drilling process. The influence of process parameters such as speed, feed rate and coolant flow rate on the surface finish and circularity were investigated during the experimentation. The experiments were conducted according to the Taguchi’s L9 array design using process parameters. The quality of the hole characteristics were measured using roughness tester and CMM. Regression analysis has been carried out for prediction of hole quality characteristics from the experimentation. It is observed that the predicted results are good correlation with measured values. Also, the results indicate that the feed rate is the most influencing parameter for drilling of AMC225xe.

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The growing demand for light weight materials for the application in the aircraft industries, aerospace industries, marine, sports and recreation industries has attracted the usage of fiber reinforced materials especially GFRP composites. Usage of these kind of materials are exponentially increasing because of its specific engineering characteristics like high strength to weight ratio. Drilling of these materials has been always a challenge because of its heterogeneous nature. Better hole quality is required for these materials for the assembly into components. In this work delamination analysis was attempted using digital image processing, experiments are carried out for 27 holes using full factorial design and it was observed that scanned images obtained using a flat bed scanner (HP, 1300 DPI) used for digital image processing, gives better visualization for delamination. The feed rate is the most influencing parameter out of the three process input parameters considered.