An ANFIS modelling and genetic algorithm-based optimization of through-hole electrical discharge drilling of Inconel-825 alloy

Inconel 825 superalloys have been frequently used in aerospace, electronic means, as well as other industrial domains due to their superior thermo-mechanical properties. The feasibility of EDD for the precision drilling of superalloys has been demonstrated. The EDD process was modelled and optimized using an intelligent technique of adaptive neuro-fuzzy inference system and genetic algorithm described in this paper. For copper and brass tubular shape electrode material, the ANFIS model was developed to explain the influence of input machining attributes such as input current, pulse on-time, pulse off-time, and electrode diameter on the response of material removal rate, electrode wear rate, taper angle, hole circularity and hole dilation at entry and exit. The usefulness of the constructed ANFIS model in predicting output quality features for the chosen input machining parameters was demonstrated. According to the results, the proposed approach significantly enhanced the machining performance in the EDD process.