Fourier convolution-parallel neural network framework with library matching for multi-tool processing decision in optical fabrication

Intelligent manufacturing of ultra-precision optical surfaces is urgent but rather difficult to be break through due to the complex physical interactions. The development of data-oriented neural network provides a new way, but the existing networks cannot be adapted for optical fabrication with high feature dimensions & small specific dataset. In this letter, for the first time, a novel Fourier convolution-parallel neural network (FCPNN) framework with library matching was proposed to realize multi-tool processing decision, including tool size & material, slurry type and removal rate. The feature dimension requirement is reduced by 3-5 orders of magnitude to achieve supervised learning with hundred-level dataset.