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Competence Centre VirtMet: Metrology for virtual measuring instruments

In the course of the digital transformation, the importance of mathematical-physical simulations and in silico experiments is increasing rapidly. If real measuring equipment and measurements are simulated with such simulations, this can be called "virtual measuring device" or "virtual measurement". In many areas these are now in everyday use. For example, simulations serve to gain a better understanding of the real experiment, to plan new experiments or to evaluate existing ones. In the meantime, simulations are increasingly being used as an essential component of a real measurement, usually as part of an inverse problem.

In this development, the task of metrology is to secure confidence in simulation results when they are used in the same way as or combined with real measurements. 

Download Initiates file downloadVirtMet Whitepaper

Scope of the PTB competence center

Already existing examples at PTB for virtual measuring instruments are, among others, the Opens internal link in current windowTilted-Wave Interferometer (TWI) or the  Opens internal link in current windowVirtuelle Koordinatenmessgerät (VCMM).

In a national workshop Opens internal link in current windowMetrology for Virtual Measuring Instruments organized by PTB in March 2018, the following overriding questions and cross-sectional tasks were identified for these and other application examples:

  1. How to ensure trust in simulation results?
  2. How can virtual and real measurements be compared?
  3. What standards are required for interfaces, metadata and data formats?
  4. How can virtual experiments for complex measurement systems with large amounts of data be handled using machine learning methods?

PTB's treatment of these issues requires continuous and intensive interdisciplinary cooperation. For this reason, the PTB Competence Center "Metrology for Virtual Measuring Instruments" was established, in which the existing expertise is bundled and the interdisciplinary exchange is continuously promoted. The centre is coordinated and supported by the working group Opens internal link in current windowPSt1 Coordination Digitalization in order to realise a possible linkage of the developments in VirtMet with other PTB projects in the field of digititalization. In addition, the competence center will further strengthen the exchange and cooperation with external partners in this area with regular workshops. 

Based on concrete working points, the higher-level questions will be dealt with in cross-departmental projects. By embedding the projects in the competence centre, there will be an intensive and regular exchange between all participants in order to exploit synergy effects and pursue a joint strategy.

Concrete projects in VirtMet

Simulation for medical imaging with X-rays

Due to the non-linear behavior of current imaging techniques using X-rays (CT, mammography), new measures are required for the evaluation of image quality. The development of such measurements and their reliable determination is crucial for an optimal balance between necessary radiation exposure and required image quality for reliable diagnostics. The development of so-called "model observers" and the characterization of their uncertainty is a active field of research. Virtual experiments can significantly support this development.

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Transfer of the VCMM concept to other areas and for use in Metrological Digital Twins

The Virtual Coordinate Measuring Machine (VCMM) developed at PTB has been used very successfully for many years in the field of coordinate measuring technology for process-accompanying measurement uncertainty determination and measurement process optimization in science and industry. The established PTB technology, which was awarded the Braunschweig IHK Technology Transfer Prize in 2005, meets the requirements of international guidelines and standards. Due to the complex structure and the time-consuming programming of necessary software modules, a transfer to other metrological areas has hardly taken place so far and is now to be promoted within the framework of VirtMet.

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Tilted-Wave Interferometer (TWI) as example for Hand-in-Hand calibration of real and virtual experiments

The optical industry uses aspheres and free-form surfaces in modern optical systems, which, however, place very high demands on metrology. Optical measurement techniques have a prominent role to play here, as they do not damage the measurement objects. The industry urgently needs traceability in optical asphere/free form metrology, which is not yet available.

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Development of a virtual flow meter

The aim of this project is to establish numerical flow simulations as a reliable and practicable tool for the calibration and uncertainty determination of flowmeters (DFM). Important work contents are CFD simulations to simulate different flow configurations and to analyze their effects on the measuring instruments. The possibility of virtually assessing flowmeters with significantly higher accuracy using simulations would not only strengthen current research cooperations, but would also enable further project partnerships. With the development described above, it would be much more advantageous for manufacturers to develop new measuring instruments and further improve existing ones.

Involved PTB groups

NameCompetence in VirtMet
Opens internal link in current windowMechanics und AcusticsDigital Twins in the area of mechanical quantities 
Opens internal link in current windowFluidsCalibration of flowmeters
Opens internal link in current windowImaging and wave opticsCalibration of optical measuring instruments
Opens internal link in current windowCoordinate measurementsVirtual coordinate measurement machine
Opens internal link in current windowMedical imagingMetrology for imaging methods
Opens internal link in current windowNeutron radiationMetrology for imaging methods
Opens internal link in current windowHeat and vaccumCalibration of flowmeters
Opens internal link in current windowMathematical modeling and data analysisSimulations, measurement uncertainty, machine learning


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