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Optimization of process parameters in magnetic field assisted powder mixed EDM of aluminium 6061 alloy

Arun Kumar Rouniyar, Pragya Shandilya

2020Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science38 citationsDOI

Abstract

Magnetic field assisted powder mixed electrical discharge machining (MFAPM-EDM) is a variant of EDM process where magnetic field coupled with electric field is used with addition of fine powder in dielectric to improve the surface quality, machining rate and stability of the process. Aluminium 6061 alloy as workpiece was selected due to growing use in aviation, automotive, naval industries. In this present work, parametric study and optimization was carried out on MFAPM-EDM machined Aluminium 6061 alloy. In this study, process parameters such as discharge current (I P ), spark duration (P ON ), pause duration (P OFF ), concentration of powder (CP) and magnetic field (MF) were considered to analyze the effect on material erosion rate (MER) and electrode wear rate (EWR). Box Behnken design approach based on response surface methodology (RSM) was utilized for performing the experiments. Quadratic model to predict the MER and EWR were developed using response surface methodology. Discharge current has most significant effect of 50.176% and 36.36% on MER and EWR, respectively among all others process parameters. Teacher-learning-based optimization (TLBO) was employed for determining the optimal process parameters for maximum MER and minimal EWR. The results obtained with TLBO was compared with well-known optimization methods such as genetic algorithm (GA) and desirability function of RSM. Minimum EWR (0.1021 mm 3 /min) and maximum MER (30.4687 mm 3 /min) obtained using TLBO algorithm for optimized process parameters was found to better as compared to GA and desirability function.

Topics & Concepts

Electrical discharge machiningMaterials scienceResponse surface methodologyAluminiumAlloyMagnetic fieldMetallurgyMachiningComposite materialMechanical engineeringComputer scienceEngineeringMachine learningPhysicsQuantum mechanicsAdvanced Machining and Optimization TechniquesAdvanced machining processes and optimizationAdvanced Surface Polishing Techniques
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