抛光
磁流变液
计算机科学
人工神经网络
丝带
人工智能
功能(生物学)
径向基函数
材料科学
光学
计算机视觉
磁场
物理
量子力学
进化生物学
复合材料
生物
作者
Kexiong Yan,Longxiang Li,Runmu Cheng,Ximing Liu,Xingchang Li,Yang Bai,Xuejun Zhang
出处
期刊:Optics Express
[The Optical Society]
日期:2024-06-19
卷期号:32 (16): 27099-27099
摘要
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.
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