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<p>Ion beam polishing (IBP) plays a critical role in achieving sub-nanometer surface accuracy for advanced optical systems. However, it faces challenges such as edge effects, uneven material removal, and low efficiency caused by the difficulty in optimizing process parameters. In this study, we first constructed a multivariate simulation model that integrates path spacing, feed velocity, and tool influence function (TIF). This model quantifies the internal interactions between processing parameters and surface topography. We then established an analytical material removal model to link processing parameters with surface quality indicators. Additionally, we proposed a general dynamic programming method to adaptively synchronize the distribution of path spacing and feed velocity, thereby enhancing both processing efficiency and surface quality. Experimental verification on our self-developed IBP equipment showed remarkable improvements in surface accuracy. The root-mean-square (RMS) error and peak-valley (PV) error decreased by 57.66% and 61.71%, significantly outperforming traditional methods (which achieved 29.00% RMS reduction and 23.50% PV reduction). By integrating analytical modeling, dynamic process optimization, and experimental validation, this study establishes a comprehensive methodology from theoretical design to practical implementation. It provides a high-precision and high-efficiency manufacturing solution for complex optical components in aerospace and semiconductor applications.</p>

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Adaptive collaborative optimization method for ion beam polishing parameters based on analytical model

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Zixuan Chen,
  • Deqing Zhu,
  • Shanyi Ma,
  • Lin Wang,
  • Mao Wang,
  • Jianguo Zhang,
  • Junfeng Xiao

摘要

Ion beam polishing (IBP) plays a critical role in achieving sub-nanometer surface accuracy for advanced optical systems. However, it faces challenges such as edge effects, uneven material removal, and low efficiency caused by the difficulty in optimizing process parameters. In this study, we first constructed a multivariate simulation model that integrates path spacing, feed velocity, and tool influence function (TIF). This model quantifies the internal interactions between processing parameters and surface topography. We then established an analytical material removal model to link processing parameters with surface quality indicators. Additionally, we proposed a general dynamic programming method to adaptively synchronize the distribution of path spacing and feed velocity, thereby enhancing both processing efficiency and surface quality. Experimental verification on our self-developed IBP equipment showed remarkable improvements in surface accuracy. The root-mean-square (RMS) error and peak-valley (PV) error decreased by 57.66% and 61.71%, significantly outperforming traditional methods (which achieved 29.00% RMS reduction and 23.50% PV reduction). By integrating analytical modeling, dynamic process optimization, and experimental validation, this study establishes a comprehensive methodology from theoretical design to practical implementation. It provides a high-precision and high-efficiency manufacturing solution for complex optical components in aerospace and semiconductor applications.