machining parameters

feed rate

cutting parameters

Results in Engineering

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 machine 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 (R 2 ) 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. • Surface Roughness of AISI 4340 alloy steel minimized effectively in practical environments. • Face-Centred CCD approach provides a good coefficient of regression. • ANN provides the best fitness model to implement the Genetic Algorithm. • Combination of ANN and Genetic Algorithm obtains the optimal CNC machining process parameters.

Optimization of CNC turning parameters using face centred CCD approach in RSM and ANN-genetic algorithm for AISI 4340 alloy steel