| 引用本文: | 宋辞,彭小强,戴一帆,等.基于去除函数预测模型的磁流变抛光工艺优化研究.[J].国防科技大学学报,2009,31(4):20-24.[点击复制] |
| SONG Ci,PENG Xiaoqiang,DAI Yifan,et al.Research on Process Optimization of Magnetorheological Finishing Basing on Predictive Model of Removal Function[J].Journal of National University of Defense Technology,2009,31(4):20-24[点击复制] |
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| 基于去除函数预测模型的磁流变抛光工艺优化研究 |
| 宋辞, 彭小强, 戴一帆, 石峰 |
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(国防科技大学 机电工程与自动化学院，湖南 长沙 410073)
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| 摘要: |
| 在分析磁流变抛光加工过程的基础上，建立了磁流变抛光的去除函数预测模型，该模型利用加工前后的面形误差分布和仿真计算的面形残差分布，针对不同材料间的去除函数模型效率系数进行辨识，能够实现去除函数模型的准确预测。以该模型为基础，通过在传统磁流变抛光工艺中加入去除函数效率系数实时辨识的工艺环节，可以对磁流变抛光的加工工艺进行优化。利用该优化工艺对一块口径202mm的HIP SiC进行9次循环迭代加工，采用子孔径拼接测量技术进行测量，面形误差由初始的PV 0.72μm,RMS 0.108μm提升到最终的PV 0.13μm,RMS 0.012μm。实验表明，去除函数预测模型能够优化磁流变抛光工艺，提高加工的确定性和增强工艺的适用性，实现光学镜面的高精度确定性磁流变抛光加工。 |
| 关键词: 磁流变抛光 去除函数模型 辨识 预测 |
| DOI： |
| 投稿日期：2009-05-06 |
| 基金项目:国家自然科学基金资助项目(50775215，50875256);国防科技大学优秀研究生创新资助项目(B090302) |
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| Research on Process Optimization of Magnetorheological Finishing Basing on Predictive Model of Removal Function |
| SONG Ci, PENG Xiaoqiang, DAI Yifan, SHI Feng |
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(College of Mechatronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| A predictive model of removal function in MRF has been established based on an analysis of MRF process. According to the residual surface error before and after finishing as well as the simulative residual surface error, this model can identify the efficiency coefficient of removal function with varieties of material and achieve exact prediction of removal function. Based on this model, the MRF process can be optimized by identifying the efficiency coefficient of removal function. A HIP SiC mirror which isin size had been polished nine times with this optimal MRF process. As a result, its residual surface error decreased from PV 0.72μm and RMS 0.108μm to PV 0.13μm and RMS 0.012μm, tested by sub-aperture stitching technique. The experiments indicate that the predictive model of removal function can optimize MRF process, improve the finishing determinacy and enhance the process flexibility so as to achieve high-precision and deterministic MRF of optical mirror. |
| Keywords: MRF removal function identification prediction |
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