Tracking error-incorporated surface topography prediction and tool path modification for slow slide servo diamond turning

In slow slide servo (SSS) diamond turning, maintaining high form fidelity becomes more challenging as cutting speed increases, mainly owing to the servo axis’s inaccurate tracking of high dynamic trajectories. However, conventional machining planning within computer-aided manufacturing (CAM) systems often neglects the impact of tracking errors on the resulting surface topography. To address this issue, this study proposes a post-processor that supplements commercial CAM software by integrating surface topography prediction and tool path modification. The servo axis’s frequency response model is first identified to predict tracking results for a given tool path. A material removal simulation incorporating tool geometry is then performed along the predicted tool path to estimate the resulting surface topography. The distribution characteristics of the predicted form errors induced by tracking errors are analyzed, and the nominal tool path is modified before machining to compensate for these form distortions. This approach bridges the gap between CAM tool path generation and actual machining by leveraging predictive insights, thereby reducing reliance on repetitive trial-and-error. Experimental validation confirms that the predicted error distributions closely match the actual results. Comparative analysis indicates that the proposed method reduces RMS tracking error by approximately 35% and RMS profile error by 48%, relative to machining without the post-processor. These findings provide a generalizable and efficient solution for enhancing form accuracy in SSS machining of complex-shaped optics, highlighting its potential for industrial adoption.