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 $430\mathrm{mm}\times 430\mathrm{mm}$ fused silica part with the initial surface error $\mathrm{PV}=1741.29\mathrm{nm}$, $\mathrm{RMS}=433.204\mathrm{nm}$. The final surface residual error in the clear aperture after two simulation steps turned out to be $\mathrm{PV}=11.7\mathrm{nm}$, $\mathrm{RMS}=0.5\mathrm{nm}$. 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 $1000\mathrm{mm}\times 1000\mathrm{mm}$ 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.

Pattern recognition (PR) methods are commonly utilized in the extraction of motion intentions from myoelectric signals, which is realized by relating several electromyogram (EMG) patterns to specific types of motion. Researchers have reported that the hand-engineering features widely used in PR-based methods can be significantly affected by external confounding factors that diminish their accuracy and robustness in clinical settings. Moreover, since only simple mapping is carried out from the feature space to the task space (involving, for the most part, discrete motion intentions), there is no opportunity to exploit fully the underlying mechanism of synergic neuromuscular control. Inspired by deep learning, we have proposed a novel convolutional neural network (CNN) structure based on the characteristics of raw EMG signals that can effectively decode complex wrist movements with three degrees of freedom (DOF) directly from raw EMG signals rather than relying on hand-engineering features. Our method has the potential to incorporate more information than other models by enlarging the training dataset. We demonstrate here that our method performs significantly better (in terms of R2) than the current state-of-art regression method (i.e., support vector regression), especially when confounding factors are involved. We further found that this CNN-based decoding method can be generalized when multiple healthy subjects are taken into account. For a new subject, our method can provide an appropriate control over 3-DOF cursor movements on a screen even without a specific training.

When used in an optical system, aspheric optics can increase the degrees-of-freedom (DOF) in a design, reduce the system’s weight and size, and provide a significant advantage for correcting system aberrations.¹ When using traditional polishing methods, the production of aspherics, especially in the polishing stage, involves several challenges such as the mismatch between the tool-surface and the work-piece, the dependency on skilled workers, the unpredictability of removal, inefficiency, and so on. Therefore, various computer-controlled optical surfacing (CCOS) processes have been developed since the 1960s.² 3. 4. 5. 6. ^{– 7} Jones⁸ successfully adopted a small-pitch-tool approach with their CCOS facility for large optics, but its removal efficiency remained low and there was an obvious edge effect. Kodak^{9 – 10} reported results on ion beam figuring (IBF) technology, which can achieve an extra-high-precision surface. However, it is well known that this technology’s material removal efficiency is particularly low. Fluid jet polishing, which is similar to IBF, was developed by Fähnle et al.¹¹ In the 1990s, Kordonski et al.¹² proposed a magnetorheological finishing process, which demonstrates a high level of predictability but is not effective for removing mid-spatial frequency errors.

The development of optical technology has increased the requirements for high-precision optical elements, especially aspheric optics. When used in an optical system, aspheric optics can increase the degrees of freedom in a design, reduce the weight and size of the system, and provide a significant advantage for correcting system aberrations.¹