Zhang Yiang, Wang Pengxiang, Guan Chaoliang, Liu Meng, Peng Xiaoqiang, Hu Hao
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
National Key Laboratory of Equipment State Sensing and Smart Support, Changsha 410073, China.
Micromachines (Basel). 2025 Jun 22;16(7):730. doi: 10.3390/mi16070730.
Magnetorheological finishing is widely used in the high-precision processing of optical components, but due to the influence of multi-source system errors, the convergence of single-pass magnetorheological finishing (MRF) is limited. Although iterative processing can improve the surface accuracy, repeated tool paths tend to deteriorate mid-spatial frequency textures, and for complex surfaces such as aspheres, traditional manual alignment is time-consuming and lacks repeatability, significantly restricting the processing efficiency. To address these issues, firstly, this study systematically analyzes the effect of six-degree-of-freedom positioning errors on convergence behavior, establishes a positioning error-normal contour error transmission model, and obtains a workpiece positioning error tolerance threshold that ensures that the relative convergence ratio is not less than 80%. Further, based on these thresholds, a hybrid self-positioning method combining machine vision and a probing module is proposed. A composite data acquisition method using both a camera and probe is designed, and a stepwise global optimization model is constructed by integrating a synchronous iterative localization algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The experimental results show that, compared with the traditional alignment, the proposed method improves the convergence ratio of flat workpieces by 41.9% and reduces the alignment time by 66.7%. For the curved workpiece, the convergence ratio is improved by 25.7%, with an 80% reduction in the alignment time. The proposed method offers both theoretical and practical support for high-precision, high-efficiency MRF and intelligent optical manufacturing.
磁流变抛光在光学元件的高精度加工中得到广泛应用，但由于多源系统误差的影响，单次磁流变抛光（MRF）的收敛性受到限制。尽管迭代加工可以提高表面精度，但重复的刀具路径往往会使中频空间纹理恶化，对于非球面等复杂表面，传统的手动对准既耗时又缺乏重复性，严重限制了加工效率。为了解决这些问题，首先，本研究系统地分析了六自由度定位误差对收敛行为的影响，建立了定位误差-法向轮廓误差传递模型，并获得了确保相对收敛率不低于80%的工件定位误差容限阈值。进一步地，基于这些阈值，提出了一种结合机器视觉和探测模块的混合自定位方法。设计了一种同时使用相机和探头的复合数据采集方法，并通过将同步迭代定位算法与非支配排序遗传算法II（NSGA-II）相结合构建了逐步全局优化模型。实验结果表明，与传统对准方法相比，该方法使平面工件的收敛率提高了41.9%，对准时间减少了66.7%。对于曲面工件，收敛率提高了25.7%，对准时间减少了80%。该方法为高精度、高效率的磁流变抛光和智能光学制造提供了理论和实践支持。
