Characterization of Hybrid Silicon Carbide and Boron Carbide Nanoparticles-Reinforced Aluminum Alloy Composites

AbstractHybrid nanocomposites based on aluminum alloy 6061 reinforced with different hybrid ratios of SiC (0.5, 1.0 and 1.5 vol. %) and B4C (fixed 0.5 vol. %) nanoparticles were successfully fabricated using ultrasonic cavitation based solidification process. The fabricated cast specimens were characterized using SEM study with EDS analysis, hardness test, tension test and impact test. The results indicate that, by the ultrasonic cavitation effects namely transient cavitation and acoustic streaming, the nano reinforcements were successfully incorporated in the aluminum matrix. SEM study with EDS validates the presence of SiC and B4C nanoparticles in the aluminum matrix. Compared to the un-reinforced alloy, the room temperature hardness and tensile strength of the hybrid composites increased quite significantly while the ductility and impact strength reduced marginally. The combination of 1.0 volume percentage SiC and 0.5 volume percentage B4C gives the superior tensile strength. The major reason for an increase in the room-temperature mechanical properties of the hybrid composites should be attributed to the larger hybrid ratio of SiC and B4C nanoparticles, the coefficient of thermal expansion mismatch between matrix and hybrid reinforcements and the dispersive strengthening effects

ANALITICAL STUDY ON COMPRESISVE STRENGTH OF REACTIVE POWDER CONCRETE

This work focuses on development of Artificial Neural Networks (ANNs) in prediction of compressive strength of reactive powder concrete after 28 days. To predict the compressive strength of reactive powder concrete nine input parameters that are cement, water, silica fume, fly ash, Ground granulated blast Furnace slag, super plasticizer, fine aggregate, Quartz sand and steel fibres are identified. A total of 35 different data sets of concrete were collected from the technical literatures. Number of layers, number of neurons, activation functions were considered and the results were validated using an independent validation data set. A detailed study was carried out, considering single hidden layers for the architecture of neural network. The performance of the 9-3-1 architecture was the best possible architecture. The results of the present investigation indicate that ANNs have strong potential as a feasible tool for predicting the compressive strength of reactive powder concrete