Computational optimization of micro‐machining parameters for maraging steel using evolutionary algorithm

Micro‐electrical discharge machining affords good machinability for maraging steel which possess excessive hardness. In order to identify the ideal drilling settings, this work suggests a hybrid optimization strategy that combines Taguchi orthogonal array design with an evolutionary genetic algorithm (GA). Regression models are developed for predicting an effective experimental design in the Taguchi orthogonal array. A multi‐objective fitness function that maximizes material removal rate (MRR) and minimizes tool wear rate (TWR) is generated in the algorithm. The investigation is done to find ideal solutions outside the experimental space. The prediction accuracy of the constructed models is strong (R 2 > 0.93). Single‐point crossover, tournament selection and adaptive convergence criteria for repeatability are all included in the framework. When compared to traditional Taguchi optimization, experimental validation shows a 14.6 % decrease in tool wear rate of 1.572 mm 3 ·min −1 and a 13 % rise in material removal rate of 0.0322 mm 3 ·min −1 by setting the parameters at voltage of 50 V, current of 4 A and pulse‐on time of 6 μs. With significant promise for aerospace and tooling applications, this hybrid methodology offers an efficient and methodical way to optimize processes for machining hard materials.