RESUMO
Intelligent manufacturing of ultra-precision optical surfaces is urgently desired but rather difficult to achieve due to the complex physical interactions involved. The development of data-oriented neural networks provides a new pathway, but existing networks cannot be adapted for optical fabrication with a high number of feature dimensions and a small specific dataset. In this Letter, for the first time to the best of our knowledge, a novel Fourier convolution-parallel neural network (FCPNN) framework with library matching was proposed to realize multi-tool processing decision-making, including basically all combination processing parameters (tool size and material, slurry type and removal rate). The number of feature dimensions required to achieve supervised learning with a hundred-level dataset is reduced by 3-5 orders of magnitude. Under the guidance of the proposed network model, a 260 mm × 260 mm off-axis parabolic (OAP) fused silica mirror successfully achieved error convergence after a multi-process involving grinding, figuring, and smoothing. The peak valley (PV) of the form error for the OAP fused silica mirror decreased from 15.153λ to 0.42λ and the root mean square (RMS) decreased from 2.944λ to 0.064λ in only 25.34 hours. This network framework has the potential to push the intelligence level of optical manufacturing to a new extreme.