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基于激光三角法的自由曲面测量。

Measurement of Free-Form Curved Surfaces Using Laser Triangulation.

机构信息

School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.

出版信息

Sensors (Basel). 2018 Oct 18;18(10):3527. doi: 10.3390/s18103527.

Abstract

Laser triangulation (LT) is widely used in many fields due to its good stability, high resolution and fast speed. However, the accuracy in these applications suffers from severe constraints on the data acquisition accuracy of LT. To solve this problem, the optical triangulation principle, the object equation of the optical path relationship and the deviation of the laser spot centroid are applied to deduce a mathematical model. Therefore, the image sensor inclination errors can be quantitatively calculated, and the collected data are compensated in real time. Further, a threshold sub-pixel gray-gravity (GG) extraction algorithm is proposed; the gradient function and Gaussian fit algorithm are used to set thresholds to remove the impact of the spot edge noise area on the center location; and polynomial interpolation is employed to enhance the data density of the traditional GG method, thus improving the data acquisition accuracy of LT. Finally, the above methods are applied to on-machine measurement of the American Petroleum Institute (API) thread and the screw rotor, respectively. The experimental results prove that the proposed method can significantly improve the measurement accuracy of free-form curved surfaces using LT and that the improved laser spot center extraction algorithm is more suitable for free-form curved surfaces with smaller curvature and more uniform curvature changes.

摘要

激光三角测量(LT)由于其良好的稳定性、高分辨率和快速速度而被广泛应用于许多领域。然而,在这些应用中,其准确性受到 LT 数据采集精度的严重限制。为了解决这个问题,应用光学三角测量原理、光路关系的物体方程和激光光斑质心的偏差,推导出一个数学模型。因此,可以定量计算图像传感器倾斜误差,并实时补偿采集的数据。进一步提出了一种阈值亚像素灰度重心(GG)提取算法;使用梯度函数和高斯拟合算法设置阈值,以消除光斑边缘噪声区域对中心位置的影响;并采用多项式插值增强传统 GG 方法的数据密度,从而提高 LT 的数据采集精度。最后,将上述方法分别应用于美国石油学会(API)螺纹和螺旋转子的机上测量。实验结果证明,所提出的方法可以显著提高 LT 对自由曲面的测量精度,并且改进的激光光斑中心提取算法更适用于曲率较小且曲率变化更均匀的自由曲面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/571f/6209907/efaea484403b/sensors-18-03527-g001.jpg

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