Tool mark prediction on the surface of large-aperture mirrors via magnetorheological finishing

The magnetorheological finishing (MRF) of surfaces often results in tool mark errors. A prediction model can effectively guide subsequent processing, necessitating thorough research. To address this issue, this paper introduces an enhanced continuous tool influence function method. This method involves sub dwell time convolution with varying tool influence functions, enabling tool mark prediction. Numerical simulations demonstrate the proposed method's effectiveness, while the data size is estimated to confirm its economic properties. Subsequently, a MRF experiment was conducted, affirming the practicability through power spectral density evaluation. A fast algorithm is given to guide tool mark predictions on large-aperture mirrors fabrication engineering subjected to sub-aperture polishing.