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基于梯度聚类离散思想的磁流变抛光自适应轨迹
引用本文:贾阳, 吉方, 张云飞, 等. 基于梯度聚类离散思想的磁流变抛光自适应轨迹[J]. 强激光与粒子束, 2015, 27: 121008. doi: 10.11884/HPLPB201527.121008
作者姓名:贾阳  吉方  张云飞  黄文
作者单位:1.中国工程物理研究院 机械制造工艺研究所, 四川 绵阳 621 900
摘    要:针对磁流变抛光过程中抛光轨迹会引入迭代误差的问题,设计了步长和行距随光学表面梯度自适应变化的光栅线抛光轨迹。首先根据光学元件的表面误差分布,利用标准五点法获得面形各点的梯度值,再基于聚类离散思想将所有面形点根据梯度值大小进行了归类,从而得到轨迹步长和行距随面形误差变化的自适应轨迹。在自研的磁流变加工机床上进行了实验研究,将一块直径50 mm的微晶玻璃,从峰谷值为65 nm、均方根值为12 nm收敛到峰谷值为21 nm、均方根值为2.5 nm,并且在加工后的表面功率谱密度曲线上没有出现明显的尖峰误差。实验结果表明,这种自适应轨迹能有效抑制中高频误差。

关 键 词:磁流变抛光   迭代误差   梯度   聚类离散   自适应轨迹
收稿时间:2015-08-12
修稿时间:2015-10-14

Adaptive tool path of magnetorheological polishing based on discrete gradient clustering
Jia Yang, Ji Fang, Zhang Yunfei, et al. Adaptive tool path of magnetorheological polishing based on discrete gradient clustering[J]. High Power Laser and Particle Beams, 2015, 27: 121008. doi: 10.11884/HPLPB201527.121008
Authors:Jia Yang  Ji Fang  Zhang Yunfei  Huang Wen
Affiliation:1. Institute of Mechanical Manufacturing Technology,CAEP,Mianyang 621900,China
Abstract:To solve the problem that tool paths will bring iterative errors in the process of magnetorheological polishing, an adaptive polishing path method is proposed, in which the step size can be adjusted according to the optical surface gradient. Firstly, according to the distribution of the original surface errors, all the gradient values at each point can be obtained and will be classified based on the discrete gradient clustering. Then a tool path that both the row and column step size can be adjusted with respect to the surface errors is obtained. Experiments on the home-made magnetorheological finishing machine MRP-1200M successfully process a 50 mm diameter microcrystalline glass, from 65 nm(PV) and 12 nm(RMS) to 21 nm(PV) and 2.5 nm(RMS) without obvious peak error in the power spectral density curve after processing. Experimental results show that this adaptive tool path can effectively suppress the mid-spatial errors and the high-spatial errors.
Keywords:magnetorheological finishing  iterative errors  gradient  clustering discrete  adaptive path
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