Grey relational analysis-based genetic algorithm optimization of electrical discharge drilling of Nimonic-90 superalloy

Abstract

Nickel-based superalloys are being increasingly used in aerospace, automotive industries due to their excellent thermo-mechanical properties. These applications require complex shapes and profiles which may not be obtained by conventional machining processes. These materials are also known as difficult-to-machine due to their excellent thermo-mechanical properties. The electrical discharge drilling proves its suitability in the precision drilling of superalloys. During, the electrical discharge drilling of these materials, hole taper, hole circularity and hole dilation are key attributes which influence the drilled hole quality. In this research paper, the experiments have been conducted by L27 orthogonal array and this experimental data have been utilized for developing the models of different geometrical quality characteristics such as hole circularity, hole taper and hole dilation. Further, a new hybrid approach grey relational analysis-based genetic algorithm has been proposed and implemented for the multi-objective optimization of different quality characteristics. The effects of different process parameters on various geometrical quality characteristics have also been discussed. Finally, the confirmation tests have been performed to validate the predicted results obtained by the proposed hybrid methodology to the experimental results. It has been observed by the comparison results that the machining performance in the electrical discharge drilling process has been remarkably improved through proposed approach.