Data-driven material removal rate estimation in bonnet polishing process

Abstract

Bonnet polishing is an ultra-precision polishing technique used for manufacturing components utilized in optics, electronics, and Scientific instrumentation, where sub-nanometer accuracy is required. However, the process is not fully deterministic and requires multiple process-metrology iterations. In modern computer numerically controlled (CNC) machines, polishing is performed by moderating the bonnet tool dwell time at each location based on the input parameters and material removal rate (MRR). While the MRR is typically treated as constant once established, it continuously evolves due to the process’s dynamic nature and changing conditions. This variability in MRR impacts the convergence of the polishing process, necessitating repeated surface processing and resulting in increased manufacturing time and cost. In this work, we present a data-driven approach to estimate the amount of material removed during the pre-polishing routine in bonnet polishing. The estimations are based on the force exerted by the bonnet tool on a polished surface along the three dimensions. Measurements were obtained using a bespoke force table with load sensors across three axes, mounted on the Zeeko IRP600 machine table. The results demonstrate the Effectiveness of this data-driven approach for estimating MRR, achieving a mean absolute error of 0.0541 µm and a mean absolute percentage error of 5.89% across the test set.