The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array

Abstract

The elliptical paraboloid array plays an important role in precision measurement, astronomical telescopes, and communication systems. The calibration of the vertex distance of elliptical paraboloids is of great significance to precise 2D displacement measurement. However, there are some difficulties in determining the vertex position with contact measurement. In this study, an elliptical paraboloid array and an optical slope sensor for displacement measurement were designed and analyzed. Meanwhile, considering the geometrical relationship and relative angle between elliptical paraboloids, a non-contact self-calibration method for the vertex distance of the elliptical paraboloid array was proposed. The proposed self-calibration method was verified by a series of experiments with a high repeatability, within 3 μ m in the X direction and within 1 μ m in the Y direction. Through calibration, the displacement measurement system error was reduced from 100 μ m to 3 μ m . The self-calibration method of the elliptical paraboloid array has great potential in the displacement measurement field, with a simple principle and high precision.

A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array

“…Liu et al [15] developed a refined coordinates mapping method to achieve camera array intrinsic and extrinsic calibration, which can improve measurement accuracy. Lv et al [16] proposed a self-calibration method for the vertex distance of an elliptical parabolic array. The system error of the displacement measurement after calibration was reduced from 100 to 3 µm.…”

Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

“…Detection of defects in micro-armatures for mobiles using deep convolution neural networks (CNNs) [17], blister defect detection using CNNs for lithium-ion batteries [18], and object detection using neural networks for identification of cracks [19], are also very good examples of practical problems tackled by the new generation computer vision and machine learning (incl. deep learning) techniques A special mention should be given to the algorithmic contributions on inline inspection of warm-die forged revolution workpieces using 3D reconstruction (car component case), since it approaches some novel concepts with industrial impact in computational geometry [20], and to the new self-calibration approach of elliptic paraboloid arrays frequently used in precision measurement [21]. A contribution on how to build knowledge graphs for industrial terminology in the automotive sector is presented in [22].…”