Magnetorheological finishing (MRF) stands out as a notable polishing technology, characterized by high precision and minimal damage. However, establishing an accurate and practical model for the tool influence function (TIF) of MRF poses a significant challenge. In this paper, a TIF modeling method of MRF based on distributed parallel neural networks is proposed for the first time. Assessment of the viability of this approach through multiple sets of robot-assisted MRF experiments is detailed. The experimental results conclusively demonstrate the successful intelligent prediction of TIF, with key indicators such as volume removal rate and peak removal rate achieving an average prediction accuracy exceeding 95%. This method can remarkably advance the intelligence of the TIF model in MRF and serve as a valuable reference for other optical fabrication methods.

This paper introduces an optimization strategy for fabricating large aspheric mirrors. We polished a large SiC aspheric mirror, 4 m in diameter, achieving a surface error of 1/40λ RMS. To the best of our knowledge, this is the first instance of such a result for a mirror of this material and size combination. Due to the various performance settings of different tools, achieving optimal polishing results with a single setting is challenging. We evaluated the performance of various tool settings and developed an optimization strategy, dividing error maps to enhance efficiency in large-aperture aspheric mirror fabrication. We established the relationship between tool size and its error control capability. The residual error map of the mirror was divided into two parts using Zernike polynomial expansion based on the frequency order of the error map. Here, we used the first 36 terms of the Zernike polynomial fit to define a low-order error map, and the residual error was used to define a high-order error map. Large tools were used to correct the low-order frequency error map, whereas small tools were used to correct the high-order frequency error map. Therefore, the original residual error map could be corrected with significantly high efficiency. By employing this strategy, we fabricated a 4 m SiC aspheric mirror in 18 months, achieving a final surface error better than 0.024λ RMS.

High-precision freeform surfaces are widely used in ultra-precision optical systems to improve imaging performance and reduce optical system weight. With the development of short wavelength optics represented by extreme ultraviolet lithography, the high-precision requirements for the full frequency errors and geometrical complexity of optical freeform surfaces pose great challenges to optical processing and are difficult to achieve by traditional manufacturing processes. This paper proposes a cross-scale surface shape error correction method to correct surface shape errors in different cycles in bonnet polishing. First, the material removal mechanism based on physical and chemical effects was discussed to obtain the method of cross-scale removal functions. Secondly, the analysis method of the frequency response characteristics of the tool based on power spectral density was discussed to analyze, evaluate, and guide the bonnet polishing process of freeform surfaces. Finally, the proposed method was verified by the experiments in processing a fused silica freeform surface of 120 mm diameter; the final root-mean-square (RMS) of surface shape accuracy was better than λ/100.

Magnetorheological Finishing (MRF) is widely regarded as an effective technique to polish and figure aspheric optics. MRF based on Robot Arm is developed by us. This new machine is more flexible, efficient, cost-effective and smaller space-usage in optical shop, compared with the traditional MRF machines. The components of MRF based on Robot Arm are introduced firstly. Position-attitude control and polishing tool path are also studied in this paper. The experiments and application of MRF based on Robot Arm demonstrate the effectiveness and validity of MRF based on Robot Arm in optical fabrication.