In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process

Abstract

The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been verified to monitor the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It has been proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.