High-convergence-ratio ion beam figuring method based on weight dynamic adjustment and parameter mapping

Ion beam figuring (IBF) has become a core process in advanced optical component manufacturing due to its atomic-level material removal capability. However, the low processing convergence ratio, caused by limitations of traditional methods and uncontrollable removal function states in vacuum environments, restricts the efficient production of high-precision optical components. This study proposes a high-convergence-ratio IBF method based on weight dynamic adjustment and parameter mapping. For different surface profile errors, an optimal removal function is selected through a processing strategy with dynamically adjusted weight coefficients. A Faraday cup is used for real-time online detection of ion beam density spatial distribution, with synchronous compensation of positioning errors to ensure data reliability. A BP neural network is introduced to establish a nonlinear mapping model between beam density parameters and removal function parameters. By precisely adjusting the aperture and stand-off distance, beam density parameters are finely matched, enabling online dynamic control of removal function parameters. Experimental results show that this method increases the IBF convergence ratio to 8.61, overcomes the constraints of vacuum environments, and provides an innovative solution for manufacturing high-precision optical components.