深度学习点衍射干涉三维坐标定位技术

为了提高现有的三维坐标定位技术的测量精度、稳定性和测量效率，提出了基于深度学习的点衍射干涉三维坐标定位方法。该方法设计了一个深度神经网络用于点衍射干涉场的坐标重构，将相位差矩阵作为输入，构建训练数据集，将点衍射源坐标作为输出，训练神经网络模型。利用训练有素的神经网络对测量到的相位分布进行初步处理，将相位信息转换为点衍射源坐标，根据得到的点衍射源坐标进一步修改粒子群算法的初始粒子，进而重构出高精度的三维坐标值。该神经网络为建立干涉场相位分布与点衍射源坐标之间的非线性关系提供了一种可行的方法，显著提高了三维坐标定位的精度、稳定性和测量效率。为验证所提方法的可行性，进行了数值仿真和实验验证，采用不同的方法进行反复对比与分析。结果表明：所提方法的单次测量时间均在0.05 s左右，其实验精度能够达到亚微米量级，重复性实验的均值和RMS值分别为0.05μm和0.05μm，充分证明了该方法的可行性，并证明了其良好的测量精度和可重复性，为三维坐标定位提供了一种有效可行的方法。

Deep-learning-based point-diffraction interferometer for 3D coordinate positioning

In order to improve the measurement accuracy, stability and efficiency of the existing 3D coordinate positioning technology, a deep-learning-based point-diffraction interferometer for 3D coordinate measurement method was proposed. A deep neural network was designed for coordinate reconstruction of the point-diffraction interference field. The phase difference matrix was used as the input to construct the training dataset, and the coordinates of point-diffraction sources were used as the output to train the neural network model. The well-trained neural network was used to process the measured phase distribution initially and the phase information was converted to the coordinates of point-diffraction sources. According to the obtained coordinates of point-diffraction sources, the initial particles of the particle swarm optimization algorithm were further modified, and then the high-precision three-dimensional coordinate was reconstructed. This neural network provides a feasible method to establish the nonlinear relationship between the phase distribution of the interference field and the coordinates of the point-diffraction sources, and significantly improves the accuracy, stability and measurement efficiency of the 3D coordinate positioning. In order to verify the feasibility of the proposed method, numerical simulation and experimental verification were carried out, and different methods were used for repeated comparison and analysis. The results show that the single measurement time of the proposed method is about 0.05 s, and the experimental accuracy can reach the submicron magnitude. The mean and RMS values of the repeatability experiments are 0.05 μm and 0.05 μm, respectively, which proves the feasibility of the proposed method and its good measurement accuracy and stability. It provides an effective and feasible method for 3D coordinate positioning.