Digital twins of measurement systems are used to estimate their measurement uncertainty. In the past, virtual coordinate measuring machines have been extensively researched. Research on digital twins of optical systems is still lacking due to the high number of error contributors. A method to describe a digital twin of an optical measurement system is presented in this article. The discussed optical system is a laser line scanner mounted on a coordinate measuring machine. Each component of the measurement system is mathematically described. The coordinate measuring machine focuses on the hardware errors and the laser line scanner determines the measurement error based on the scan depth, in‑plane angle and out‑of‑plane angle. The digital twin assumes stable measurement conditions and uniform surface characteristics. Based on the Monte Carlo principle, virtual measurements can be used to determine the measurement uncertainty. This is demonstrated by validating the digital twin on a set of calibrated ring gauges. Two validation tests are performed: the first verifies the virtual uncertainty estimation by comparison with experimental data. The second validates the measured diameter of different ring gauges by comparing the estimated confidence interval with the calibrated diameter.
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Robot CT systems bring great flexibility to X-ray CT based inspection scans. The system geometry needs to be well calibrated in order to avoid serious artifacts in the reconstructed volume. However, conventional calibration methods are normally designed for circular paths, which may be insufficient for a robot CT system. This paper proposes a phantom based geometric qualification method which supports nearly any view points. This is achieved by automatically mapping 3D-2D features based on collinear markers. The position and orientation of the X-ray focal spot and the detector are determined for each image of the reference phantom. This can be used for the CT reconstructions of later scans with the same trajectory. It is also possible to use the acquired geometries parameters to calibrate the underlying robots. The approach is validated on experimentally acquired data and the uncertainty is estimated by a Monte-Carlo based method.
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