Aluminium (Al) is the suitable material for aerospace and automotive industries due its light weight, corrosion resistance, weldability, non-magnetic and mechanical properties. But, machining of Al and its alloy and finding the suitable tool is really a big challenge because of its formation of BUE (Built-up Edge) and BUL (Built-up Layer). This paper presents the influence of cutting parameters (speed, feed and depth of cut) and its effect on the cutting force and the surface finish. Five different advanced cutting tool inserts (SPUN WC, SPGN WC, PCD, WC ? TiN and WC ? Ti(C, N) TiN ? Al 2 O 3 ) at different cutting speed (V c ) ranging between 300 m/min and 700 m/min and feed rate (f) of 0.045, 0.06, 0.09 and 0.125 mm/rev at a depth of cut of 0.2 mm (constant throughout the experiment) were taken for the experiment. Tool inserts were characterized by Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analysis. The cutting forces were measured using Kistler force dynamometer. Amongst all tools, PCD provided a better result in all aspects but surprisingly WC tool provided a better surface finish with lesser tool wear. For all cutting conditions, high speed (670 m/min) and low feed rate (0.045 mm/rev) were recommended. adhana(0123456789().,-volV)FT3 ](0123456789().,-volV)
Burrs are bottleneck of precision machining and automation production. Burrs are formed in every edges and faces, during the turning process, which affects the quality level of surface roughness. In this paper the experimental study of EN3 low carbon steel were carried out to minimize the surface roughness using response surface methodology and genetic algorithm. Tungsten Carbide was used as a cutting tool for this turning operation. Machined samples are examined under Scanning Electron Microscope (SEM) for burr formation. A wide variety of analysis between cutting parameters have been shown graphically. The minimization of burr was achieved and hence better surface quality was obtained by optimizing the cutting parameters like cutting speed, feed, and depth of cut, with the aid of Genetic Algorithm (GA) & Response Surface Methodology (RSM) Techniques.
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