AI-Driven Optimization of Advanced Machining for Haste alloy by ANFIS Method

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of 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 primarily examines the use of Inconel 625 in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. 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 statistical significance of process parameters that impact performance indicators. The proposed regression models for Inconel 625 are more versatile, efficient, and accurate in comparison to the current models, providing enhanced monitoring capabilities. The updated models have been verified, demonstrating a robust link between empirical data and projected results.