Metrology, Volume 2, Issue 2 (June 2022) – 10 articles

The usage of virtual instruments (VIs) to analyze measurements and calculate uncertainties is increasing. Well-known examples are virtual coordinate measurement machines (VCMMs) which are often used and even commercially offered to assess measurement uncertainties of CMMs. A more recent usage of the VI concept is posed by the modeling of scatterometers. These VIs can be used to assess the measurement uncertainty after the measurement has been performed based on the real measurement data or prior to the measurement to predict the measurement uncertainty using a type of simulated measurement data. The research question addressed in this paper is to assess if this predicted uncertainty will be similar in magnitude to the calculated uncertainty based on the measurement data. It turns out that this is not necessarily the case. The main observation of this paper was that the uncertainty predicted by a VI can be highly sensitive to the chosen way of operating the VI. To amend this situation, a simple procedure was proposed that can be used prior to performing the real measurement and that is believed to produce a conservative prediction of the measurement uncertainty in most cases. This was verified in a case study involving the measurement of the asphericity of an imperfect sphere using a CMM, with the uncertainty calculated by means of a VCMM. Full article

Accurate frequency measurement plays an important role in many industrial and robotic systems. However, different influences from the application’s environment cause signal noises, which complicate frequency measurement. In rough environments, small signals are intensively disturbed by noises. Thus, even negative Signal-to-Noise Ratios (SNR) are possible in practice. Thus, frequency measuring methods, which can be used for low SNR signals, are in great demand. In previous work, the method of cross-correlation spectrum has been developed as an alternative to Fast Fourier-Transformation or Continuous Wavelet Transformation. It is able to determine the frequencies of a signal under strong noise and is not affected by Heisenberg’s uncertainty principle. However, in its current version, its creation is computationally very intensive. Thus, its application to real-time operations is limited. In this article, a new way to create the cross-correlation spectrum is presented. It is capable of reducing the calculation time by 89% without significant accuracy loss. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with an SNR of −14 dB and a signal length of 2000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean-square error of 0.21% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended. Full article

Modern manufacturing processes are characterized by growing product diversities and complexities alike. As a result, the demand for fast and flexible process automation is ever increasing. However, higher individuality and smaller batch sizes hamper the use of standard robotic automation systems, which are well suited for repetitive tasks but struggle in unknown environments. Modern manipulators, such as collaborative industrial robots, provide extended capabilities for flexible automation. In this paper, an adaptive ROS-based end-to-end toolchain for vision-guided robotic process automation is presented. The processing steps comprise several consecutive tasks: CAD-based object registration, pose generation for sensor-guided applications, trajectory generation for the robotic manipulator, the execution of sensor-guided robotic processes, test and the evaluation of the results. The main benefits of the ROS framework are readily applicable tools for digital twin functionalities and established interfaces for various manipulator systems. To prove the validity of this approach, an application example for surface reconstruction was implemented with a 3D vision system. In this example, feature extraction is the basis for viewpoint generation, which, in turn, defines robotic trajectories to perform the inspection task. Two different feature point extraction algorithms using neural networks and Voronoi covariance measures, respectively, are implemented and evaluated to demonstrate the versatility of the proposed toolchain. The results showed that complex geometries can be automatically reconstructed, and they outperformed a standard method used as a reference. Hence, extensions to other vision-controlled applications seem to be feasible. Full article

Rough surfaces such as metal additive manufactured surfaces are quite challenging for measurement. Artifacts caused by irregular and difficult-to-measure geometries are inevitable. Removing all the artifacts would cause a portion of surface information to be missing. Different from previous works, the postprocessing in this paper includes an additional step to eliminate artifacts based on autocorrelation functions of particular subimages instead of simply removing them. This increases the accuracy with respect to surface roughness and provides a more comprehensive view on the topography. In addition, a dome shape LED array ring light is proposed to provide all-round lighting due to the high degree of irregularity of workpiece surfaces. The experimental results obtained from FVM are validated and compared with the given roughness values of a Rubert Microsurf 329 comparator test panel as well as measurement results of a metal additive workpiece by a confocal microscope. Full article

Large-volume metrology is essential to many high-value industries and contributes to the factories of the future. In this context, we have developed a tri-dimensional coordinate measurement system based on a multilateration technique with self-calibration. In practice, an absolute distance meter, traceable to the SI metre, is shared between four measurement heads by fibre-optic links. From these stations, multiple distance measurements of several target positions are then performed to, at the end, determine the coordinates of these targets. The uncertainty on these distance measurements has been determined with a consistent metrological approach and it is better than 5 µm. However, the propagation of this uncertainty into the measured positions is not a trivial task. In this paper, an analytical solution for the uncertainty assessment of the positions of both targets and heads under a multilateration scenario with self-calibration is provided. The proposed solution is then compared to Monte-Carlo simulations and to experimental measurements: it follows that all three approaches are well agreed, which suggests that the proposed analytical model is accurate. The confidence ellipsoids provided by the analytical solution described well the geometry of the errors. Full article

This paper concerns the assessment of railway track surface conditions in relation to the degree of weed infestation. The paper conceptually describes the proposed method using a visual system to analyse weed infestation level. The use of image analysis software for weed detection is also proposed. This new measurement method allows for a mobile assessment of the track’s weed infestation status. Validation of the assessment method in real conditions will allow for further expansion of the system using new shades of green from the RAL palette, and will take into account a more extensive and detailed assessment of weed infestation on the track in accordance with applicable railway regulations. Full article

Spectral analysis is successfully adopted in several fields. However, the requirements and the constraints of the different cases may be so varied that not only the tuning of the analysis parameters but also the choice of the most suitable technique can be a difficult task. For this reason, it is important that a designer of a measurement system for spectral analysis has knowledge about the behaviour of the different techniques with respect to the operating conditions. The case that will be considered is the realization of a numerical instrument for the real-time measurement of the spectral characteristics of a multi-tone signal (amplitude, frequency, and phase). For this purpose, different signal processing techniques can be used, that can be classified as parametric or non-parametric methods. The first class includes those methods that exploit the a priori knowledge about signal parameters, such as the spectral shape of the signal to be processed. Thus, a self-configuring procedure based on a parametric algorithm should include a preliminary evaluation of the number of components. The choice of the right method among several proposals in the literature is fundamental for any designer and, in particular, for the developers of spectral analysis software, for real-time applications and embedded devices where time and reliability constrains are arduous to fulfil. Different aspects should be considered: the desired level of accuracy, the available elaboration resources (memory depth and processing speed), and the signal parameters. The present paper details a comparison of some of the most effective methods available in the literature for the spectral analysis of signals (IFFT-2p, IFFT-3p, and IFFTc, all based on the use of an FFT algorithm, while improving the spectral resolution of the DFT with interpolation techniques and three parametric algorithms—MUSIC, ESPRIT, and IWPA). The methods considered for the comparison will be briefly described, and references to literature will be given for each one of them. Then, their behaviour will be analysed in terms of systematic contribution and uncertainty on the evaluated frequencies of the spectral tones of signals created from superimposed sinusoids and white Gaussian noise. Full article

One of the main challenges in designing information fusion systems is to decide on the structure and order in which information is aggregated. The key criteria by which topologies are constructed include the associativity of fusion rules as well as the consistency and redundancy of information sources. Fusion topologies regarding these criteria are flexible in design, produce maximal specific information, and are robust against unreliable or defective sources. In this article, an automated data-driven design approach for possibilistic information fusion topologies is detailed that explicitly considers associativity, consistency, and redundancy. The proposed design is intended to handle epistemic uncertainty—that is, to result in robust topologies even in the case of lacking training data. The fusion design approach is evaluated on selected publicly available real-world datasets obtained from technical systems. Epistemic uncertainty is simulated by withholding parts of the training data. It is shown that, in this context, consistency as the sole design criterion results in topologies that are not robust. Including a redundancy metric leads to an improved robustness in the case of epistemic uncertainty. Full article

In modern power systems, the integration of renewable energy sources relies on dedicated inverters whose power electronic circuitry switches at high frequencies and causes conducted emissions in the supraharmonic range, i.e., from 9 to 150 kHz. In this regard, the normative framework is still lacking a reference measurement method as well as a set of emission limits and performance requirements. From a metrological point of view, it is important to evaluate whether some of the power quality indices adopted for radiated emissions could be transposed also in this context. In particular, the paper considers a recent algorithm for the identification of supraharmonic components and discusses how its estimates affect the estimation of quasi-peak values. To this end, the paper describes the implementation of a fully digital approach and validates the results by means of an experimental comparison against a traditional quasi-peak detector. The proposed analysis confirms the potential of the considered approach and provides some interesting insights about the reliability of quasi-peak estimation in supraharmonic range. Full article

Ensuring a high accuracy when measuring the parameters of devices under testing is an important task when conducting research in the terahertz-frequency range. The purpose of this paper is a practical study of the thermal drift errors of a vector network analyzer using low-terahertz-frequency extender modules. For this, the change in the measurement error, which is a function of time, was analysed using system, based on Keysight N5247B vector network analyzer and covering the frequency ranges of 220–330 GHz, 500–750 GHz, and 750–1100 GHz. The results of our experiment showed that the measurement error decreased rapidly during the first half hour of warm-up and stabilized by 3 h after turning on the equipment. These results allow for an estimation of the necessary warm-up time depending on the requirements for the measurement’s accuracy. This makes it possible to optimize the experiment and reduce its duration. Full article