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<p>This study investigates the optimal selection and performance evaluation of Nano Powder Mixed Dielectric Fluids (NPMDF) for use in Electric Discharge Machining (EDM) through a hybrid multi-criteria decision-making (MCDM) approach. Nanoparticles, including Cr₂O₃, Al₂O₃, TiO₂, MnO₂, and ZrO₂, were dispersed in DEF-92 dielectric fluid at concentrations of 0.5%, 1%, and 5%, with particle sizes ranging from 40 to 100&#xa0;nm. Ultrasonic homogenization ensured uniform dispersion, while characterization through Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) confirmed particle distribution and composition. Thermal conductivity analysis revealed that Al₂O₃ and ZrO₂-based nanofluids exhibited the highest thermal conductivities, making them optimal for heat dissipation in EDM. The study employed five different MCDM techniques, CoCoSo, VIKOR, TOPSIS, CODAS, and MOORA for the selection of optimal nanofluid based on criteria such as thermal conductivity, viscosity, and heat capacity. The hybrid fuzzy TOPSIS method validates the optimal selection of EDM-2 nanofluid, confirming the selection of CoCoSo and VIKOR methods. The hybrid MCDM method incorporating fuzzy logic improved decision-making by addressing uncertainties and providing refined rankings, thus demonstrating its effectiveness in optimizing dielectric fluid selection for EDM applications.</p>

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Selection of nano powder mixed dielectric fluid (NPMDF) in electric discharge machining using hybrid multi-criteria decision-making method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月29日

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  • Dharmendra Kumar,
  • Mithilesh K. Dikshit,
  • Neetu Chaudhary,
  • Vimal Kumar Pathak,
  • Ramanpreet Singh

摘要

This study investigates the optimal selection and performance evaluation of Nano Powder Mixed Dielectric Fluids (NPMDF) for use in Electric Discharge Machining (EDM) through a hybrid multi-criteria decision-making (MCDM) approach. Nanoparticles, including Cr₂O₃, Al₂O₃, TiO₂, MnO₂, and ZrO₂, were dispersed in DEF-92 dielectric fluid at concentrations of 0.5%, 1%, and 5%, with particle sizes ranging from 40 to 100 nm. Ultrasonic homogenization ensured uniform dispersion, while characterization through Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) confirmed particle distribution and composition. Thermal conductivity analysis revealed that Al₂O₃ and ZrO₂-based nanofluids exhibited the highest thermal conductivities, making them optimal for heat dissipation in EDM. The study employed five different MCDM techniques, CoCoSo, VIKOR, TOPSIS, CODAS, and MOORA for the selection of optimal nanofluid based on criteria such as thermal conductivity, viscosity, and heat capacity. The hybrid fuzzy TOPSIS method validates the optimal selection of EDM-2 nanofluid, confirming the selection of CoCoSo and VIKOR methods. The hybrid MCDM method incorporating fuzzy logic improved decision-making by addressing uncertainties and providing refined rankings, thus demonstrating its effectiveness in optimizing dielectric fluid selection for EDM applications.