Data-Driven Optimization of Titanium Grade 7 Machining for Automotive Applications

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. With the rising demand for superior products and the necessity for quick design modifications, decision-making in the industrial sector becomes increasingly complex. This study specifically examines Titanium Grade 7 and suggests creating prediction models through regression analysis to estimate performance measurements in ECM. The experiments are formulated based on Taguchi's ideas, utilizing a multiple regression approach to deduce mathematical equations. The Taguchi method is utilized for single-objective optimization in order to determine the ideal combination of process parameters that will maximize the material removal rate. ANOVA is a statistical method used to determine the relevance of process factors that affect performance measures. The suggested prediction technique for Titanium Grade 7 exhibits superior flexibility, efficiency, and accuracy in comparison to current models, providing expanded monitoring capabilities. The validated models demonstrate a robust link between empirical data and projected results. This study investigates the potential uses of Titanium Grade 7 in the automotive industry, highlighting its importance in sectors that need strong materials for challenging conditions.