Mapping model of ribbon contour and tool influence function based on distributed parallel neural networks in magneto-rheological finishing

Abstract

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.