Given these considerations, a novel material removal model considering the normal error of the polishing tool was developed based on contact mechanics, kinematic theory, and the abrasion mechanism. In combination with the polishing trajectory, the surface morphology and form accuracy after polishing were predicted under different normal-error conditions. A series of experiments was conducted to verify the model and clarify the influence mechanism of the normal error on the surface topography in CCOS. The established model provides a solid theoretical basis for the optimization of robot motion control strategies.
3. Experimental DesignThe experimental research presented in this section combined robotic machining technology with the CCOS process to achieve the high-efficiency and high-precision polishing of large-diameter aspheric optical parts. Figure 3 shows the experimental robotic polishing device for the CCOS process, which consisted of a six-degrees-of-freedom hybrid robot system, a polishing slurry supply and circulation system, a polishing head, and a computer numerical control (CNC) system. During the experiment, the polishing slurry was pressurized by the slurry supply pump and then injected into the center hole of the polishing tool, and the rotational speed, pressure, and motion trajectory of the polishing tool were controlled by the robot numerical control system. The K9 optical glass sample with a diameter of 120 mm and a curvature of 0.01198 was polished by cerium oxide polishing slurry with a concentration of 15% and a particle size of 1 μm. A polyurethane polishing tool with a radius of 10 mm was used in the experiment, which had a revolution speed of 60 rpm, a rotation speed of 600 rpm, a load of 0.15 Mpa, and an eccentricity of 4 mm. The polished samples were measured with a Nanovea white-light interferometer with a sampling spacing of 20 μm.To study the influence of the robot motion accuracy on the machining accuracy of the optical components, we carried out processing experiments using polishing tools with different normal errors for experimental groups A, B, and C. As shown in Figure 4, the polishing paths of the three groups of experiments were discretized into 11 × 11 machining traces. The position and normal vector of each processing track point could be calculated according to the geometric features of the optical element to determine the polishing position and attitude control when the polishing robot scanned each track point. In the group A polishing experiment, the normal error of the polishing tool when the polishing robot scanned each track point was 0. In the group B polishing experiment, the normal error of the polishing tool at different track points was randomly assigned values between −0.5° and 0.5°. In the group C polishing experiment, the normal error of the polishing tool at different track points was also randomly generated, but its value was kept between −1° and 1°. Figure 5a,b present the random normal-error distributions generated for the group B and C polishing experiments, respectively. 4. Results and DiscussionFigure 6a shows the theoretically calculated surface topography of a 40 × 40 mm polishing pad. Figure 6b presents a partial schematic diagram of the distribution of micropores on the pad surface (5 mm × 5 mm), with micropores of different sizes being evenly distributed on the pad surface. The surface porosity of the polishing pad obtained by simulation was 35.8%, and the error between the simulated distribution and the measurement results was within the allowable range. The surface of the actual polishing pad had a 2 × 1.5 mm diversion groove and a slurry supply hole with a diameter of 4 mm in the center, so the simulated pad surface morphology is shown in Figure 6c. Figure 6d presents a photograph of a 40 mm diameter porous polyurethane polishing pad, showing that the pore distribution characteristics of the simulated surface were basically consistent with the real structure.Figure 7 shows the three-dimensional material removal characteristics of planetary-motion polishing with different normal errors. The simulation results showed that when the normal error was 0, the removal topography of the material presented a perfect axisymmetric distribution. However, when the normal error increased to 0.5°, the material removal topography was skewed, and the maximum removal depth increased. When the normal phase error continued to increase to 1°, the material removal profile exhibited a severe unilateral collapse, and the material removal along the inclined side increased significantly. The reason for this was that the force on the material surface in the single-spot removal was uneven due to the error of the normal accuracy, resulting in the uneven removal of the material, which eventually increased the error of the actual machined surface relative to the ideal surface.Figure 8 shows the experimental and simulated surface profiles for different polishing-tool normal errors. When the normal error of the polishing tool was 0°, 0.5°, and 1°, the PV values of the simulated surfaces were 3.89, 4.97, and 4.7 μm, which showed good agreement with the experimentally measured PV values of 3.97, 4.09, and 4.43 μm, respectively. At the same time, the RMS values of the simulated surfaces were 0.500, 0.659, and 0.507 μm, which were also very consistent with the RMS values of the experimentally measured surfaces of 0.593, 0.620, and 0.583 μm, respectively. The experimental results showed that as the normal error increased from 0° to 0.5° and 1°, the PV values of the surface profile of the optical element decreased from 5.42, 5.28, and 4.68 μm to 3.97, 4.09, and 4.43 μm, respectively. The corresponding convergence rates were 26.8%, 22.5%, and 5.3%. The RMS values decreased from 0.754, 0.895, and 0.678 μm to 0.593, 0.620, and 0.583 μm, with corresponding convergence rates of 21.4%, 30.7%, and 14.0%, respectively. According to the comparative analysis of the PV and RMS numerical convergence rates of the three sets of polishing experiments, as the normal error of the polishing tool gradually increased, the overall convergence rate of the PV and RMS values of the optical element after polishing decreased. The convergence rate of the RMS value was relatively high under the normal error of 0.5°, which may have been caused by the surface error distribution of convex and concave in the group B optical elements. The established theoretical model successfully predicted the influence of the normal error on the form accuracy. According to the analysis of the surface error of the experimental and simulated polishing surface, the higher the normal error of the polishing robot, the greater the reduction in the contour accuracy of the surface after polishing and the larger the decrease in the surface convergence efficiency in the practical polishing process.Figure 9 shows the PSD distribution of the polished surface for three different polishing-tool normal errors. The results showed that the motion accuracy of the polishing robot had a substantial influence on the medium- and high-frequency errors after polishing. As the normal error of the polishing robot gradually increased, the relative changes in the medium- and high-frequency errors before and after polishing gradually decreased, because the random change in the normal error of the polishing robot caused an uneven force to be exerted on each point of the polishing pad as the polishing process progressed, resulting in an uneven material removal rate and the suppression of medium- and high-frequency errors. Therefore, the higher the motion accuracy of the polishing robot, the higher the convergence efficiency of the optical element surface. Additionally, the mid- and high-frequency errors of the optical element could be reduced to a certain extent. 5. ConclusionsThis paper presented a study involving the modeling, simulation, and testing of surface generation considering the motion accuracy of the polishing device. A theoretical model was developed to predict and simulate the effect of different normal errors on surface generation. The simulation results showed that when the normal error increased from 0° to 1°, the removal topography of the material changed from perfect axisymmetric distribution to severe unilateral collapse, and the material removal along the inclined side increased significantly.
A series of surface polishing tests was conducted by combining robotic machining technology with CCOS technology to verify the theoretical model under different normal-error conditions. The experimental data reasonably agreed with the simulation results. The results also indicated that as the normal error increased from 0° to 0.5° and 1°, the peak-to-valley (PV) values of the surface profile of the optical element decreased from 5.42, 5.28, and 4.68 μm to 3.97, 4.09, and 4.43 μm, respectively. The corresponding convergence rates were 26.8%, 22.5%, and 5.3%. The root mean square (RMS) values decreased from 0.754, 0.895, and 0.678 μm to 0.593, 0.620, and 0.583 μm, with corresponding convergence rates of 21.4%, 30.7%, and 14.0%, respectively. Furthermore, the improvement of the mid- and high-frequency errors was significantly weakened. Therefore, the influence of the robot motion accuracy on the surface integrity of the polished workpiece needs to be further examined in future research, and it is necessary to study robot motion control strategies to improve the surface convergence efficiency.
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