Forecasting the Surface Quality in the Machining Process of C35E Steel

Considered an important measurement parameter in the machining industry, surface roughness has a fundamental role in ensuring the quality of the final product. In turning operations, existing approaches to predicting surface quality rely heavily on factors related to tool-workpiece interaction, heat, and material. The objective of this research is to create a forecasting model that will help analyze the effect of the rake angle on surface quality when machining C35E steel. Taguchi's factorial design methodology is used in the experimental design. The parameters selected for this study are cutting speed, cutting depth, feed rate, and angle of attack. Using a conventional lathe and a P25 carbide tool, a set of 30 experimental data on C35E steel was used in this research. In order to evaluate surface roughness during the turning process and compare the experimental results with the predicted results, a linear regression model is used. To determine the accuracy of the predicted values, the coefficient of determination, the regression graph, and the mean square error were used. This research then presents a learning model that can predict surface roughness by mainly modifying the angle of attack when machining C35E steel. The ideal choice of this angle will improve the efficiency of turning C35E steel and increase the quality of the finished components. The application of the model thus developed demonstrates its reliability, as the discrepancy between the experimental and predicted results is negligible.