Abstract
In the present study a multi response optimization method using utility concept is proposed for electrical discharge machining (EDM) of AA 6061/10%Al2O3 AMMC. In the present study the required material is fabricated using stir casting process and characterization is done using SEM and EDX. The machining characteristics that are being investigated are material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) and overcut (OC) along with surface topography of the drilled holes. Taguchi’s L27 orthogonal array design is used for experimentation. The Investigation indicated that material removal rate, tool wear rate, overcut and surface roughness increases with increase in pulse-on time and peak current. But duty factor has less effect on these responses. Multi-response optimization with utility concept provides the collective optimization of both responses for improving the mean of the process.
Highlights
Metal matrix composites (MMCs) possess high specific strength and stiffness compared to common structural materials
It was found that significant machining parameters affecting the performance measures metal removal rate (MRR), surface roughness (SR) and tool wear rate were identified such as discharge current, and dielectric flushing pressure (Pali, Kumar, & Singh, 2016)
In this research, the experiments have been planned and conducted in order to investigate the effects of cutting parameters on material removal rate, tool wear rate, overcut and surface roughness in the electrical discharge machining (EDM) process
Summary
Metal matrix composites (MMCs) possess high specific strength and stiffness compared to common structural materials. In experimental study of EDM of 10%Al2O3/Al metal matrix composites, it was found that the significant factors that affect material removal rate (MRR) and tool wear rate (TWR) have a direct relationship with current and an inverse relationship with pulse on-time (Sindhu, Batish, & Kumar, 2013). It was found that significant machining parameters affecting the performance measures metal removal rate (MRR), surface roughness (SR) and tool wear rate were identified such as discharge current, and dielectric flushing pressure (Pali, Kumar, & Singh, 2016). Response surface methodology (RSM) and artificial neural network (ANN) approach was used for the process modelling and optimisation of wire electric discharge machining (WEDM) of SiCp/6061 Al metal matrix composite (MMC). The obtained results indicated that applied hybrid approaches in present study of EDD process were reasonable and substantiates with facts of improvement in MRR by 30.80%, Ra by 24.81% and Ca by 26.09% (Yadav & Yadava, 2015)
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