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- W3129163114 abstract "Machinability contribution to manufacturing is significant and its role for optimum has to be determined. Aluminum particulate reinforced compound has demand in some growing fields such as automotive, aircraft and locomotive industries. The research work focuses on machinability and optimization of machining parameters on Aluminum 6063 alloy reinforced with silicon carbide known as aluminum compound. Three specimens A, B, C reinforced with silicon carbide 3%, 6%, and 9%, respectively, were fabricated using stir cast. For each specimen, 13 experiments were conducted with varying input parameters such as speed, feed, and cutting depth in CNC vertical machining center using 10 mm end mill cutter. The recorded output responses are surface roughness and material removal rate. Using Design-Expert software’s RSM method, number of runs and regression equations were decided. The Turbo C program utilized and carried out the sorting of output measurement. The more focus is laid on the significance of machinability. The objective is to enhance parametric optimization using particle swarm optimization (PSO) method to calculate the fitness value corresponding to speed, feed, and cutting depth. The Multiobjective optimization of the input parameters obtained from particle swarm optimization found to have optimum to measure the output responses of surface finish process and output responses of material removal rate of the both are compared." @default.
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- W3129163114 date "2021-01-01" @default.
- W3129163114 modified "2023-10-14" @default.
- W3129163114 title "Experimental Investigation and Optimization of Machining Parameters of Aluminum Composite Material" @default.
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- W3129163114 doi "https://doi.org/10.1007/978-981-15-8319-3_103" @default.
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