Process Parameter Prediction for Advanced Machining of Copper-Nickel Alloy Turbine Components

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study focuses on Inconel 718 and suggests creating predictive models to anticipate performance metrics in ECM through regression analysis. The experiments are formulated based on Taguchi's principles, and a multiple regression model is utilized to deduce the mathematical equations. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the relevance of process parameters that impact performance indicators. The proposed prediction technique for Inconel 718 is more adaptable, efficient, and accurate in comparison to current models, providing enhanced monitoring capabilities. The updated models have been verified, demonstrating a robust link between empirical data and projected results.