Optimizing Titanium Alloy Grade 7 Machining with Grey Relational Analysis

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. Due to the growing need for superior products and the requirement for quick design adjustments, decision-making in production has become more complex. This study focuses on Titanium Grade 5 and suggests creating predictive models utilizing a Taguchi-grey technique to achieve multi-objective optimization in ECM. The trials are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously maximize several performance indicators. This entails optimizing the pace at which material is removed, decreasing the roughness of the surface, and attaining precise geometric tolerances. ANOVA is used to assess the relevance of process variables that affect these measures. The suggested predictive technique for Titanium Grade 5 outperforms current models in terms of flexibility, efficiency, and accuracy, providing improved capabilities for monitoring and control. In addition, the research investigates the use of Titanium Grade 5 in automotive applications, emphasizing its importance in industries that require strong materials for conditions that are prone to corrosion. The experimental validation confirms a strong correlation between the projected results and the actual performance, thereby confirming the effectiveness of the suggested approach.