Advanced Parameter Forecasting for Titanium Grade 19 Machining in Automotive Applications

Electrochemical machining (ECM) is a remarkably effective technique for producing detailed designs in materials that can conduct electricity, regardless of their level of hardness. As the desire for high-quality products and the necessity for rapid design changes grow, decision-making in the industrial sector becomes increasingly intricate. This work focuses on Titanium Grade 9 and proposes the development of prediction models using regression analysis to estimate performance measurements in ECM. The experiments are designed using Taguchi's methodology, employing a multiple regression approach to produce mathematical equations. The Taguchi technique is utilized for the purpose of single-objective optimization in order to determine the optimal combination of process parameters that will optimize the rate at which material is removed. ANOVA is a statistical method used to assess the relevance of process factors that impact performance indicators. The suggested prediction technique for Titanium Grade 9 exhibits higher flexibility, efficiency, and accuracy in comparison to existing models, providing improved monitoring capabilities. The validated models demonstrate a robust link between empirical data and expected outcomes. This study investigates the possible uses of Titanium Grade 9 in the automotive sector, with a focus on its significance in industries that demand robust materials for demanding environments.