Surface profile modelling of inclined-axis polishing based on the microscopic material removal behaviour of abrasive particles

As the final step in surface finishing, the polishing plays a critical role in determining the quality and precision of the target surface. Despite extensive research efforts centered on the Preston Equation for polishing processes, accurately capturing material removal at the zero-velocity point remains challenging. While some studies have explored material removal based on single abrasive particle behavior, these investigations have primarily focused on traditional flat polishing. There is a notable lack of research that examines material removal from the perspective of a single abrasive particle to predict polishing profiles in inclined-axis spherical polishing head applications. Therefore, this research establishes a surface profile model of inclined-axis fixed point polishing for spherical polishing head based on material removal of single abrasive particle. In which the abrasive particle sweep cross-section versus position relationships are considered, the abrasive particle movement trajectories are calculated, and multiple abrasive particle footprints overlapping strategy are set up. Polishing experiments were carried out, varying key parameters such as vertical feed depth, inclined angle, and dwell time. The outcomes reveal that the minimum error for predicting the cross-section maximum profile depth is only 6.25 %, and the contact profile is only 5.68 %, proves the precision of the model. In additions, the results also reveal that the best prediction condition for the model of vertical feed depth ≤ 0.2 mm, the inclined angle ≥ 15°, which is also the parameter condition for the formation of the approximate Gaussian profile during the experiment. The developed model is expected to not only enhance understanding of interaction mechanisms between abrasive particle- workpiece, including particle motion trajectories, but also offer practical guidance for optimizing polishing parameters and predicting surface outcomes in industrial manufacturing applications.