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In this paper, aluminium metal matrix composites were synthesized through in situ process in which aluminium alloy 5052 (AA5052) and titanium carbide were used as matrix and reinforcement materials, respectively. The microstructural characterization and formation of stable TiC phases were analyzed with the help of field emission scanning electron microscope, X-ray diffraction analysis, respectively. The 9% TiC-reinforced MMCs had shown a considerable improvement, i.e. 32% increase in hardness, 78% in ultimate tensile strength and 116% increase in yield strength when compared with the base alloy. The tensile fracture of the specimens shows dimples, voids, cracks, and ridges indicating the brittle nature. Further, the dry sliding wear properties of the composites were studied with the help of a pin-on-disc wear testing machine. The composite with 9% TiC exhibited a decrease in volumetric wear loss by 24% when compared with the base alloy at a load of 30 N. With increase in the TiC content and applied load, the COF values decreased linearly for the composites. The 9% TiC-reinforced composites show an abrasive mode of wear mechanism as a result of formation of deep grooves with no plastic deformation. With the improvement obtained in the wear properties, this metal matrix composite can be considered as a replacement for the conventional brake disc material used in the automobile industry.

In this paper an attempt has been made for linear and non linear modeling of resin bonded sand mould system using full factorial design of experiments and response surface methodology, respectively. It is important to note that the quality of castings produced using the resin bonded sand mould system depends largely on properties of moulds, which are influenced by the characteristics of sand, like type of sand, grain fineness number, grain size distribution and quantity and type of resin, catalyst, curing time etc. In the present study, percentage of resin, percentage of hardener, number of strokes and curing time are considered as input parameters and the mould properties, such as compression strength, shear strength, tensile strength and permeability are treated as responses. In the present work, phenol formaldehyde is used as the resin whereas tetrahydrophthalic anhydride as the hardener. A two level full factorial and three level central composite designs are utilized to develop input-output relationships. Surface plots and main effects plots are used to study the effects of amount of resign, amount of hardener, number of strokes and curing time on the responses, namely, compression strength, tensile strength, shear strength and permeability. Moreover, the adequacies of the developed models have been tested using analysis of variance. The prediction accuracy of the developed models have been tested with the help of twenty test cases and found reasonably good accuracy.

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In this work, vertically grown rod type ZnO nanostructures have been synthesized on metallic nickel tube films fabricated through the cost-effective process of electroforming..

This paper presents the nature-inspired genetic algorithm (GA) and particle swarm optimization (PSO) approaches for optimization of fermentation conditions of lipase production for enhanced lipase activity. The central composite non-linear regression model of lipase production served as the optimization problem for PSO and GA approaches. The overall optimized fermentation conditions obtained thereby, when verified experimentally, have brought about a significant improvement (more than 15 U/gds (gram dry substrate)) in the lipase titer value. The performance of both optimization approaches in terms of computational time and convergence rate has been compared. The results show that the PSO approach (96.18 U/gds in 46 generations) has slightly better performance and possesses better convergence and computational efficiency than the GA approach (95.34 U/gds in 337 generations). Hence, the proposed PSO approach with the minimal parameter tuning is a viable tool for optimization of fermentation conditions of enzyme production.

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