Non-sequential optimization technique for a computer controlled optical surfacing process using multiple tool influence functions.
Non-sequential optimization technique for a computer controlled optical surfacing process using multiple tool influence functions.
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DOI：
10.1364/oe.17.021850
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发表时间：
2009-11
期刊：
影响因子：
3.8
通讯作者：
Dae Wook Kim;Sug-Whan Kim;J. Burge
中科院分区：
文献类型：
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作者：
Dae Wook Kim;Sug-Whan Kim;J. Burge
光学表面可以通过计算机控制的光学表面处理(CCOS)精确地成形，这种表面处理使用由数控(NC)机床驱动的具有良好特性的亚直径抛光工具。抛光工具的运动被优化，以根据所需的移除和校准的工具影响函数(TIF)改变抛光机在工件上的停留时间。用体积小且特性非常好的TIF操作CCOS取得了很好的性能，但需要很长的时间。通过执行从大工具开始到小工具结束的顺序抛光运行，可以减少总体抛光时间。在本文中，我们提出了这种技术的一种变体，它使用一组不同大小的TIF，但优化是全局执行的，即同时优化整个抛光运行集的停留时间和工具形状。因此，实际的抛光运行将是连续的，但优化是全面的。由于优化方法从经典的方法改进为综合的非顺序算法，性能的改善是显著的。对于典型的抛光运行，我们显示了计算效率从约88%提高到约98%，从剩余均方根(RMS)表面误差提高到大约89%，从大约47%提高到大约89%。
Optical surfaces can be accurately figured by computer controlled optical surfacing (CCOS) that uses well characterized sub-diameter polishing tools driven by numerically controlled (NC) machines. The motion of the polishing tool is optimized to vary the dwell time of the polisher on the workpiece according to the desired removal and the calibrated tool influence function (TIF). Operating CCOS with small and very well characterized TIF achieves excellent performance, but it takes a long time. This overall polishing time can be reduced by performing sequential polishing runs that start with large tools and finish with smaller tools. In this paper we present a variation of this technique that uses a set of different size TIFs, but the optimization is performed globally - i.e. simultaneously optimizing the dwell times and tool shapes for the entire set of polishing runs. So the actual polishing runs will be sequential, but the optimization is comprehensive. As the optimization is modified from the classical method to the comprehensive non-sequential algorithm, the performance improvement is significant. For representative polishing runs we show figuring efficiency improvement from approximately 88% to approximately 98% in terms of residual RMS (root-mean-square) surface error and from approximately 47% to approximately 89% in terms of residual RMS slope error.