ABSTRACT
Deterministic optics fabrication using sub-aperture tools has been vital for manufacturing precision optical surfaces. The fabrication process requires the tool influence function and the tool path to calculate the dwell time that guides the tool to bring surface quality within tight design tolerances. Widely used spiral and raster paths may leave excess waviness from the tool path, and the unavoidable constant removal layer is added to obtain positive dwell time. This waviness can be removed by either using smaller tools sequentially or randomizing the tool path. However, the existing tool-path solutions can hardly adapt to different surface aperture shapes and localized surface errors. Process efficiency and accuracy are also not well considered in tool-path planning. We propose an innovative zonal Random Adaptive Path (RAP) to solve these problems in this study. Firstly, RAP can be flexibly adapted to different surface aperture shapes by introducing part boundary. Secondly, an average threshold strategy is used in the RAP planning to improve efficiency, enabling the surface errors to be selectively corrected. Finally, the threshold is performed in several passes within one processing cycle, each with its RAP, until the desired residual is achieved. The performance of the proposed RAP is studied by comparing it with the conventional tool paths. The results demonstrated that RAP takes the least processing time and achieves the best surface quality, which verifies the effectiveness of RAP in deterministic optics fabrication.
ABSTRACT
Surface errors directly affect the performance of optical systems in terms of contrast and resolution. Surface figure errors at different surface scales are deterministically removed using controlled material removal rate (MRR) during a precision optics fabrication process. We systematically sectioned the wide range of MRR space with systematic parameters and experimentally evaluated and mapped the MRR values using a flexible membrane-polishing tool. We performed numerical analysis with a tool influence function model using a distributed MRR-based Preston's constant evaluation approach. The analysis procedure was applied to a series of experimental data along with the tool influence function models to evaluate removal rates. In order to provide referenceable survey data without entangled information, we designed the experiments using Taguchi's L27 orthogonal array involving five control parameters and statistically analyzed a large number of programmatic experiments. The analysis of variance showed that the most significant parameters for achieving a higher MRR are the spot size and active diameter.
