Development of an Online Surface Roughness and Perpendicularity Prediction (OSRPP) System Using Vibration Data in CNC Vertical Machining

This study presents the development of an Online Surface Roughness and Perpendicularity Prediction (OSRPP) system aimed at enhancing quality monitoring in CNC milling and drilling operations. Vibration signals were captured using a tri-axial accelerometer during drilling and end- milling processes on aluminum 6061 workpieces. To reflect physical inspection strategies, the raw signals were segmented to match surface roughness measurement areas, while upper- and lower- hole regions were analyzed separately to represent perpendicularity. Twelve time- and frequency- domain features were extracted per axis and processed to reduce the dimension to the top five most relevant for each target output. Predictive models were developed using Multiple Linear Regression and Artificial Neural Networks (ANN), with linear regression achieving testing accuracy of 96.06% for surface roughness and 92.22% for perpendicularity, while the ANN improved performance to 99.67% and 95.98%, respectively. The results demonstrate that integrating vibration-based sensing with machine learning enables accurate online prediction of surface roughness and perpendicularity, contributing to more autonomous and data-driven decision-making in advanced manufacturing environments.