Prediction of Surface Roughness of Monel k 500 Super Alloy by Using Artificial Neural Network

• Published in: Materials science forum Vol. 1098; pp. 41 - 50
• Main Authors: Ganesh, V. Dilli, Bommi, R.M
• Format: Journal Article
• Language: English
• Published: Trans Tech Publications Ltd 29.09.2023
• Subjects: ANN; Prediction Error; Surface Roughness; Monel K500; Scaled Conjugate Gradient
• ISSN: 0255-5476; 1662-9752; 1662-9752
• DOI: 10.4028/p-QaGi3U

The surface roughness is a feature that is of tremendous relevance in the assessment of cutting performance, and it plays an essential part in the manufacturing process as well. In this research, an effort was made to construct a model based on artificial neural networks to replicate the hard turning of Monel K 500 in dry conditions. The results of this endeavor are presented. This model is anticipated to accurately estimate the surface roughness for various cutting settings. Networks that use Scaled Conjugate Gradient (SCG) were trained using a set of training data for several cycles. Then they were tested with a collection of input/output data that was specifically reserved for this purpose. For each of the designs that were considered, the root mean square error was calculated. As compared with other models, the RMSE that the SCG Produces better value-. Analysis was done on the ability of the ANN model to predict surface roughness (Ra). It was discovered that the predictions produced by the ANN model had a high degree of congruence with the experiment’s findings.