Predictive Grey-ANFIS Model for High-Precision Machining of Nimonic Alloy in Automobiles

The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Incoloy 800HT material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Incoloy 800HT is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's principle and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that affects the intended performance criteria. In order to improve the precision of forecasts, numerous regression models are created and performance indicators are formulated. A validation test was performed to confirm the results achieved through the use of the ANFIS methodology. The test findings indicate that the proposed strategy surpasses previous methodologies to a significant degree.