Taguchi-Grey Hybrid Method for Enhanced Advanced Machining of Cupronickel Alloys

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. With the growing demand for superior products and the increasing necessity for quick design modifications, decision-making in the manufacturing industry becomes increasingly complex. The primary objective of this work is to concentrate on Inconel 718 and suggest the creation of predictive models through the utilization of a Taguchi-grey technique for the purpose of multi-objective optimization in ECM. The trials follow Taguchi's principles and utilize a Taguchi-grey relational analysis (GRA) technique to maximize numerous performance indicators concurrently. This involves optimizing the pace at which material is removed while decreasing the roughness of the surface and obtaining precise geometric tolerances. ANOVA is a statistical method used to determine the importance of process factors that influence these measurements. The suggested predictive technique for Inconel 718 is superior to existing models in terms of flexibility, efficiency, and accuracy, providing improved capabilities for monitoring and control. Furthermore, the study investigates the potential uses of Inconel 718 in the automotive industry, highlighting its importance in sectors that demand durable materials in corrosive settings. The experimental validation confirms a robust association between the anticipated results and the actual performance, thereby confirming the efficacy of the suggested approach.