Abstract:
AISI 4340 alloy steel is used in a wide range of industrial applications due to its improved hardness, toughness,
fatigue, and wear resistance. The surface roughness of AISI 4340 alloy steel components in a typical CNC ma chine is minimized using CNC machining parameters such as feed rate, rotational speed, and depth of cut. The
Coded and Actual Empirical model was generated using Face Centred Central Composite Design (CCD) approach
in Response Surface Methodology (RSM) to forecast the predicted values. The machining parameters interactions
are studied using three-dimensional surface plots, and optimal process parameters are predicted with the
desirability graph. The Artificial Neural Network (ANN) approach is employed to increase the coefficient of
regression (R2
) and to get the well-trained best fitness model for the Genetic Algorithm (GA). The confirmation
test results explore experimental surface roughness value, and the percentage of error is less in the Genetic
Algorithm than Response Surface Methodology for AISI 4340 alloy steel components. As a result, this research
suggests using a combination of Artificial Neural Network (ANN) and Genetic Algorithm (GA) methodology to
find the best machining process parameters and get a good performance response in practical applications.
