Modeling of the static tool influence function of bonnet polishing based on FEA

Wang, Cheng; Wang, Z.; Xu, Yang; Sun, Zaihong; Peng, Yunfeng; Guo, Yin Biao; Xu, Qiao

doi:10.1007/s00170-014-6004-3

Cited by 55 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al 27 presented simulation results of static and dynamic TIFs. Wang et al 28 demonstrated and modeled three kinds of static TIFs based on the finite element analysis method. As in the practical polishing process in which the four discrete precession polishing modes 14 are usually adopted and the rotation speed of A-axis is zero, the four discrete precession static tool influence function (sTIF d ) should be used in the simulation polishing process to make the simulation closer to the actual.…”

Section: Tif Modelmentioning

confidence: 99%

“…Without loss of generality, the TIF of bonnet polishing is mostly modeled on a flat surface. 27,28,32 Figure 2 demonstrates the schematic diagram of the discrete precession bonnet polishing. Here, Q is a point in the contact area, ω 1 is the rotation speed of the H-axis, v r is the velocity of point Q derived from the rotation about the H-axis, ω 2 is the rotation speed of the A-axis, v p is the velocity of point Q derived from the rotation about the A-axis, O 1 is the center of the bonnet tool, O 2 is the center of the contact area, l is the z offset of the bonnet, R 0 is the radius of the bonnet tool, and α is the precession angle.…”

Section: Tif Modelmentioning

confidence: 99%

“…In bonnet tool polishing, much attention has been paid to the tool influence function (TIF), 14,27,28 the edge effect, 21,22 the final surface error, 5,19 and so on. However, little attention has been paid to the surface residual error induced by the superposition of the TIF.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of parameters for bonnet polishing based on the minimum residual error method

Wang

Yang

et al. 2014

Opt. Eng

View full text Add to dashboard Cite

For extremely high accuracy optical elements, the residual error induced by the superposition of the tool influence function cannot be ignored and leads to medium-high frequency errors. Even though the continuous computer-controlled optical surfacing process is better than the discrete one, which can decrease this error to a certain degree, the error still exists in scanning directions when adopting the raster path. The purpose of this paper is to optimize the parameters used in bonnet polishing to restrain this error. The formation of this error was theoretically demonstrated and will also be further experimentally presented using our newly designed prototype. Orthogonal simulation experiments were designed for the following five major operating parameters (some of them are normalized) at four levels: inner pressure, z offset, raster distance, H-axis speed, and precession angle. The minimum residual error method was used to evaluate the simulations. The results showed the impact of the evaluated parameters on the residual error. The parameters in descending order of impact are as follows: raster distance, z offset, inner pressure, H-axis speed, and precession angle. An optimal combination of these five parameters among the four levels considered, based on the minimum residual error method, was determined.

show abstract

Section: Tif Modelmentioning

confidence: 99%

Section: Tif Modelmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of parameters for bonnet polishing based on the minimum residual error method

Wang

Yang

et al. 2014

Opt. Eng

View full text Add to dashboard Cite

show abstract

“…The TIF of BP is defined as the average material removal in the unit time. The simulation of TIF based on this model has been reported on some literature [28][29][30][31].…”

Section: A Tif Of Bpmentioning

confidence: 99%

Dwell-time algorithm for polishing large optics

et al. 2014

View full text Add to dashboard Cite

The calculation of the dwell time plays a crucial role in polishing precision large optics. Although some studies have taken place, it remains a challenge to develop a calculation algorithm which is absolutely stable, together with a high convergence ratio and fast solution speed even for extremely large mirrors. For this aim, we introduced a self-adaptive iterative algorithm to calculate the dwell time in this paper. Simulations were conducted in bonnet polishing (BP) to test the performance of this method on a real 430 mm × 430 mm fused silica part with the initial surface error PV=1741.29 nm, RMS=433.204 nm. The final surface residual error in the clear aperture after two simulation steps turned out to be PV=11.7 nm, RMS=0.5 nm. The results confirm that this method is stable and has a high convergence ratio and fast solution speed even with an ordinary computer. It is notable that the solution time is usually just a few seconds even on a 1000 mm × 1000 mm part. Hence, we believe that this method is perfectly suitable for polishing large optics. And not only can it be applied to BP, but it can also be applied to other subaperture deterministic polishing processes.

show abstract

“…In the past few decades, computer controlled optical surfacing (CCOS) has been widely and successfully applied to the manufacture of optical components [1][2][3], providing a deterministic material removal technology for optical devices [4], such as small-sized optical lenses, large astronomical telescopes and high-power laser systems. Different processing methods are commonly used, which has a broad coverage including CNC polishing, gasbag polishing, magnetorheological polishing, ion beam polishing, etc [5][6][7] In some extreme optical systems like large-aperture telescope systems or nanoscale lithography systems, surface errors of the optical components play a critical role in the imaging and operation quality of the entire system. In consequences, study on the formation mechanism and suppression method of surface errors is of great significance to the process and manufacturing.…”

Section: Introductionmentioning

confidence: 99%

A predictable smoothing evolution model for computer-controlled polishing

Hou

Pengli

Liu

et al. 2020

J. Eur. Opt. Soc.-Rapid Publ.

View full text Add to dashboard Cite

Quantitative prediction of the smoothing of mid-spatial frequency errors (MSFE) is urgently needed to realize process guidance for computer controlled optical surfacing (CCOS) rather than a qualitative analysis of the processing results. Consequently, a predictable time-dependent model combining process parameters and an error decreasing factor (EDF) were presented in this paper. The basic smoothing theory, solution method and modification of this model were expounded separately and verified by experiments. The experimental results show that the theoretical predicted curve agrees well with the actual smoothing effect. The smoothing evolution model provides certain theoretical support and guidance for the quantitative prediction and parameter selection of the smoothing of MSFE.

show abstract

Modeling of the static tool influence function of bonnet polishing based on FEA

Cited by 55 publications

References 16 publications

Optimization of parameters for bonnet polishing based on the minimum residual error method

Optimization of parameters for bonnet polishing based on the minimum residual error method

Dwell-time algorithm for polishing large optics

A predictable smoothing evolution model for computer-controlled polishing

Contact Info

Product

Resources

About