Calibration method of laser scanning measurement system for large-scale component using a free-form surface reconstruction model. (31st March 2023)

• Record Type: Journal Article
• Authors: Gu, Rongrui; Wang, Zhongyu; Ma, Liqun; Nie, Jing
• Is Part Of: Measurement. Volume 210(2023)
• Publication Date: 2023-03-31
• Subjects: Calibration method -- Surface reconstruction -- Uncertainty analysis -- Large-scale components -- Point cloud data; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112511
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Measurement points are reconstructed to a parametric surface with uncertainty. Reconstruction model is used as a reference to calibrate the scanning device. The uncertainty is determined by both measurement device and surface reconstruction. Abstract: For large dimensional scanning measurement, the calibration method is limited by the standard part with the scale in millimeters or centermeters. In this paper, a novel calibration method suitable for large-scale components in meters has been developed based on the free-form surface reconstruction model. The Non-Uniform Rational B-Splines (NURBS) surface is approximated from the discrete measurement points obtained by high-precision detection using the least-squares method. The deviation between the measurement points and reference model is defined by statistical results and represented by the standard deviation of the distances from the measurement points to the corresponding closest points on the surface. Furthermore, the uncertainty components introduced in the procedure of coordinate registration and surface approximation are considered to estimate the combined uncertainty of a reconstructed surface. Finally, a scanning measurement device can be calibrated by the reconstruction model consisting of a NURBS surface expression and its uncertainty prediction. To validate the proposed calibration method, the surface of a standard part with a 4-meter diameter is detected to acquire a set of measurement points and subsequently reconstructed to a parametric surface, the uncertainty of which is analyzed considering errors introduced by both device accuracy and data processing. For the reconstruction model with degree of 4, the result shows that the calibration accuracy is superior to 30 μm with a tolerance error of less than 12 μm.