Surface Roughness Prediction and Parameter Optimization CNC utilizing the Response Surface Method to convert EN8 steel and Advanced Regression Models

This study examines Improving machine parameter selection for turning operations enhance manufacturing efficiency and product quality. The research focuses on key factors including Spindle speed, feed rate, and depth of cut effects on important performance indicators includes elements including surface quality, machining time, and material removal rate finish, with a particular focus on its importance it significantly influences component functionality, Durability against wear, fatigue resistance and corrosion resistance investigation employs various analytical techniques Using Response Surface Method, Taguchi Method, and Artificial Neural Networks to model and predict optimal machine parameters. EN8 steel, widely used in manufacturing shafts, crankshafts, and connecting rods, serves as the primary material under examination. The research highlights that conventional machining techniques often struggle to meet quality requirements for advanced materials, necessitating the use of Computer Numerical Control (CNC) machines for precision operations. Statistical prediction methods are implemented to develop reliable models that can determine optimal machining parameters without extensive experimental testing. The study demonstrates that proper selection of cutting conditions significantly improves surface roughness, while appropriate modelling techniques enhance the efficiency of mechanical processes, particularly when manufacturers face multiple conflicting objectives.