Prediction of Surface Roughness of a Nimonic C-238 by Using the ANN Technique

Artificial Neural Network (ANN) approach is employed to forecast surface roughness (Ra)in the turning of Nimonic C238 using cutting parameters such as Feed, Speed, and Depth of cut. Experiments using carbide inserts were carried out, and the outcomes of these experiments were measured. The measured responses were fed into an ANN, which was then trained to predict the surface roughness. It has been discovered that ANN models provide better surface roughness (Ra) predictions than mechanical assessment techniques. Furthermore, the outcomes of ANN predictions are compared to the surface roughness (Ra)value obtained through experiments. Finally, it was established that the stylus measuring technique is less accurate than ANN models in determining surface roughness. These forecasts are useful for real-time operation control, which is required to obtain the required surface roughness. The created ANN approach can accurately estimate the Surface roughness (Ra)value of the Surface roughness (Ra)with an error percentage of less than 3.45%.