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 19 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 19 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 19 in the automotive sector, with a focus on its significance in industries that demand robust materials for demanding environments.