Enhanced Aero Engine Components Manufacturing through Predictive Models for Cupronickel Machining

Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The preliminary intention of this work is to concentrate on Cupronickel and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations. Moreover, the study investigates the use of Cupronickel in automotive applications, emphasizing its crucial function in industries that demand resilient materials in harsh settings. The experimental validation has confirmed a strong correlation between the projected results and the actual performance, hence confirming the effectiveness of the ANFIS-based strategy.