Surface Roughness Analysis
Surface roughness is one of predominant parameter by which a quality of a product is determined. Surface roughness is fine irregularities on the face of surface texture where machining process feed marks were also been considered [1]. Productivity of the machine tool and other machined components are evaluated and measured using the surface finish of product. [2]
In manufacturing industries, Metal cutting is one of the most important processes in area of material removal. The quality each component that undergoes machining process is measured and then evaluated by their close adheres to product specification which includes diameter, width, length, reflective index and their outlook without any damage. Among various process conditions during
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In recent days, MMC’s are produced using various technical specifications and techniques in order to meet the demand in the market that includes properties such as higher strength and toughness, higher wear resistance, less weight and density and also to withstand thermal stability at lowest possible cost. [7]
Aluminium and its alloys have a continuous contribution in the development of any metal matrix composites because they are the key material as a matrix in development of MMC. This is mainly due to the effect of unique properties at lowest cost comparing with other materials. Al-Si based composites are generally used due to its attractive properties such as high strength vs. weight ratio, good resistance to corrosion, thermal conductivity ability, workability and excellent casting ability.
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In this research work, prediction of minimum surface roughness using optimized machining parameters with a minimum machining time period. From this research, effect of cutting parameter feed and depth of cut are directly proportional to surface roughness and cutting speed is inversely proportional. Based on these experimental results, empirical relations were formed. [15, 16]
PSO is a technique developed from simulation of the social behavior of group of birds flocking for their food. These are used for continuous type nonlinear functions for the process of optimization. In PSO, there is no need for encoding the variables for optimization. PSO will be more efficient when optimization is carried out for two or more variables. It gains more attention nowadays because of their ability that promising optimization and realistic in nature and because of its easier implementation and lesser time installation.
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