Application of ANFIS Approach for Prediction of Process Parameters for Electrochemical Machining of Titanium Grade 9 Alloys for Auto parts

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 9 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 9 provides more flexibility, efficiency, and accuracy in comparison to conventional approaches, allowing for greater monitoring and control in ECM operations. Moreover, the study investigates the potential uses of Titanium Grade 9 in the automotive industry, emphasizing its crucial function in sectors that demand resilient materials in corrosive environments. The experimental validation demonstrates a strong correlation between the projected results and the actual performance, confirming the effectiveness of the ANFIS-based strategy.