Advanced AI Tools for Haste alloy Machining: ANFIS Approach to Advanced Machining Optimization

The objective of this research is to develop an optimization method for the Electrochemical Machining (ECM) process on Inconel 625 material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Inconel 625 is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards. In order to improve the accuracy of predictions, numerous regression models are created and performance metrics are constructed. A validation test was performed to authenticate the findings acquired through the ANFIS methodology. The test results indicate that the proposed strategy is considerably more efficient than earlier approaches.