The repository administrator has not yet configured an RDF license.
Multi-objective Optimization of MWCNT Mixed Electric Discharge Machining of Alâ��30SiCp MMC Using Particle Swarm Optimization
In the present research work, the multi-walled carbon nanotube (MWCNT) mixed electric discharge machining of Alâ��SiCp-based MMC has been proposed. The effect on MWCNT concentration, peak current, pulse duration, and duty cycle on the surface roughness and material removal rate has been investigated and multi-objective optimization of MWCNT mixed-EDM process parameters has been carried out for the machining of Alâ��30SiCp substrate using particle swarm optimization (PSO) technique. The SR and MRR increased with peak current and pulse duration in the case of EDM, but SR decreased and MRR increased with the dispersion of MWCNTs in EDM dielectric fluid. The empirical model has been developed by response surface methodology to interpret the relation between input parameters and output characteristics such as SR and MRR. However, the impacts of MWCNT mixed-EDM parameters on SR and MRR are clashing in nature; there is no single condition of machining parameters, which gives the best machining quality. Multi-objective particle swarm optimization technique was used to find the best optimal condition of MWCNT mixed-EDM parameters to minimize the SR and maximize the MRR. The best global solution where, maximum MRR (1.134 mm3/min) and minimum SR (1.097 Î¼m) obtained from the Pareto optimal front is at peak current = 15.59 A, pulse-on = 169.61 Î¼s, duty cycle = 65.17, and MWCNT powder concentration = 4.08 g/l. The MRR and SR are increased by 14.89 and 15.94, respectively, after mixing 4.08 g/l MWCNT concentration in dielectric fluid. From the above study, it is recommended for the process engineer to use the proposed optimal setting to achieve maximum MRR and minimum SR. © 2018, Springer Nature Singapore Pte Ltd.
Materials Horizons: From Nature to Nanomaterials
HTML Summary of #10589 Multi-objective Optimization of MWCNT Mixed Electric Discharge Machining of Alâ��30SiCp MMC Using Particle Swarm Optimization