Grey Relational Analysis for Multi-Objective Optimization in Aerospace Machining of Titanium Grade 19

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, irrespective of their hardness. Due to the growing need for superior products and quick design adjustments, decision-making in production has become increasingly complex. This study focuses on Titanium Grade 9 and proposes creating predictive models using a Taguchi-grey technique to achieve multi-objective optimization in ECM. The experiments are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously optimize several performance indicators, including the material removal rate, surface roughness, and geometric tolerances. ANOVA is employed to assess the significance of process variables affecting these measures. The proposed predictive technique for Titanium Grade 9 outperforms current models in terms of flexibility, efficiency, and accuracy, providing enhanced capabilities for monitoring and control. Additionally, the research explores the use of Titanium Grade 9 in automotive applications, highlighting its importance in industries that require strong, corrosion-resistant materials. Experimental validation confirms a strong correlation between the projected results and actual performance, thereby demonstrating the effectiveness of the proposed approach.