Wang Yuezong, Qu Daoduo, Wang Shengyi, Chen Jiqiang, Qiu Lina
Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Micromachines (Basel). 2023 Apr 28;14(5):963. doi: 10.3390/mi14050963.
In the realm of automatic wire-traction micromanipulation systems, the alignment of the central axis of the coil with the rotation axis of the rotary stage can be a challenge, which leads to the occurrence of eccentricity during rotation. The wire-traction is conducted at a micron-level of manipulation precision on micron electrode wires; eccentricity has a significant impact on the control accuracy of the system. To resolve the problem, a method for measuring and correcting the coil eccentricity is proposed in this paper. First, models of radial and tilt eccentricity are established respectively based on the eccentricity sources. Then, measuring eccentricity is proposed by an eccentricity model and microscopic vision; the model is used to predict eccentricity, and visual image processing algorithms are used to calibrate model parameters. In addition, a correction based on the compensation model and hardware is designed to compensate for the eccentricity. The experimental results demonstrate the accuracy of the models in predicting eccentricity and the effectiveness of correction. The results show that the models have an accurate prediction for eccentricity that relies on the evaluation of the root mean square error (RMSE); the maximal residual error after correction was within 6 μm, and the compensation was approximately 99.6%. The proposed method, which combines the eccentricity model and microvision for measuring and correcting eccentricity, offers improved wire-traction micromanipulation accuracy, enhanced efficiency, and an integrated system. It has more suitable and wider applications in the field of micromanipulation and microassembly.
在自动线牵引微操纵系统领域，线圈中心轴与旋转台旋转轴的对准可能是一项挑战，这会导致旋转过程中出现偏心现象。线牵引是在微米级电极丝上以微米级操纵精度进行的；偏心对系统的控制精度有重大影响。为解决该问题，本文提出一种测量和校正线圈偏心的方法。首先，基于偏心源分别建立径向偏心和倾斜偏心模型。然后，通过偏心模型和微观视觉提出测量偏心的方法；该模型用于预测偏心，视觉图像处理算法用于校准模型参数。此外，设计了基于补偿模型和硬件的校正方法来补偿偏心。实验结果证明了模型预测偏心的准确性和校正的有效性。结果表明，模型对偏心有准确预测，这依赖于均方根误差（RMSE）的评估；校正后的最大残余误差在6μm以内，补偿率约为99.6%。所提出的将偏心模型与微观视觉相结合来测量和校正偏心的方法，提高了线牵引微操纵的精度、效率，并实现了系统集成。它在微操纵和微装配领域有更合适且更广泛的应用。
