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Ziel des Projektes war, mit Hilfe von Computersimulationen die Brennfleckgröße, das Spektrum und die erreichbare Dosisleistung von Transmissionsröhren vorherzusagen sowie diese Kenngrößen zu optimieren. Hierbei sollten insbesondere die Materialien und Schichtdicken von Substrat und strahlungserzeugender Schicht untersucht werden. Eine Voraussetzung für die Überprüfung der Ergebnisse ist die reproduzierbare, korrekte messtechnische Erfassung von Brennfleckgrößen unterhalb 5 μm.
Die Mikro- und Nanotechnologie gehört zu den Schlüsseltechnologien des 21. Jahrhunderts mit hohen Wachstumsprognosen, wie auch die im Auftrag des BMBF durchgeführte Studie “Nanotechnologie als wirtschaftlicher Wachstumsmarkt” von 2004 ausführlich darstellt. Aus diesem Trend resultiert ein steigender Bedarf an Messsystemen, die Nanostrukturen prozessnah bzw. im Fertigungsprozess charakterisieren können. Virtuelle Messgeräte liefern Erkenntnisse zur Entwicklung neuartiger Messsysteme, Analyse und Optimierung bestehender Verfahren sowie die Bestimmung der Messunsicherheit und modellbasierten Korrektur systematischer Fehler. Der virtuelle Messprozess umfasst neben dem Messmittel auch die Probe und die Wechselwirkungen zwischen beiden. In diesem Beitrag werden virtuelle Messgeräte vorgestellt sowie deren Anwendung diskutiert.
Within the framework of the European project PICASSO, the radiographic simulator aRTist (analytical Radiographic Testing inspection simulation tool) developed by BAM has been extended for reliability assessment of film and digital radiography. NDT of safety relevant components of aerospace industry requires the proof of probability of detection (POD) of the inspection. Modeling tools can reduce the expense of such extended, time consuming NDT trials, if the result of simulation fits to the experiment. Our analytic simulation tool consists of three modules for the description of the radiation source, the interaction of radiation with test pieces and flaws, and the detection process with special focus on film and digital industrial radiography. It features high processing speed with near–interactive frame rates and a high level of realism. A concept has been developed as well as a software extension for reliability investigations, completed by a user interface for planning automatic simulations with varying parameters and defects. Furthermore, an automatic image analysis procedure is included to evaluate the defect visibility. The radiographic modeling from 3D CAD of aero engine components and quality test samples are compared as a precondition for real trials. This enables the evaluation and optimization of film replacement for application of modern digital equipment for economical NDT and defined POD.
The lack of traceability to meter of X-ray Computed Tomography (CT) measurements still hinders a more extensive acceptance of CT in coordinate metrology and industry. To ensure traceable, reliable, and accurate measurements, the determination of the task-specific measurement uncertainty is necessary. The German guideline VDI/VDE 2630 part 2.1 describes a procedure to determine the measurement uncertainty for CT experimentally by conducting several repeated measurements with a calibrated test specimen. However, this experimental procedure is cost and effort intensive. Therefore, the simulation of dimensional measurement tasks conducted with X-ray computed tomography can close these drawbacks. Additionally, recent developments towards a resource and cost-efficient production (“smart factory”) motivate the need for a corresponding numerical model of a CT system (“digital twin”) as well. As there is no standardized procedure to determine the measurement uncertainty of a CT system by simulation at the moment, the project series CTSimU was initiated, aiming at this gap. Concretely, the goal is the development of a procedure to determine the measurement uncertainty numerically by radiographic simulation. The first project (2019-2022), "Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation - CTSimU" developed a framework to qualify a radiographic simulation software concerning the correct simulation of physical laws and functionalities. The most important outcome was a draft for a new guideline VDI/VDE 2630 part 2.2, which is currently under discussion in the VDI/VDE committee. The follow-up project CTSimU2 "Realistic Simulation of real CT systems with a basic-qualified Simulation Software" will deal with building and characterizing a digital replica of a specific real-world CT system. The two main targets of this project will be a toolbox including methods and procedures to configure a realistic CT system simulation and to develop tests to check if this replica is sufficient enough. The result will be a draft for a follow-up VDI/VDE guideline proposing standardized procedures to determine a CT system's corresponding characteristics and test the simulation (copy) of a real-world CT system which we call a "digital twin".
Various software products for the simulation of industrial X-ray radiography have been developed in recent years (e.g., aRTist 2, CIVA CT, Scorpius XLab, SimCT, Wilcore) and their application potential has been shown in numerous works. However, full systematic approaches to characterise a specific CT system for these simulation software products to obtain a truthful digital twin are still missing. In this contribution, we want to present two approaches to obtain realistic grey values in X-ray projections in aRTist 2 simulations based on measured projections. In aRTist 2, the displayed grey value of a pixel is based on the energy density incident on that pixel.
The energy density is calculated based on the X-ray tube spectrum, the attenuation between source and detector as well as an energy-dependent sensitivity curve of the detector. The first approach presented in this contribution uses the sensitivity curve as a free modelling parameter. We measured the signal response at different thicknesses of Al EN-AW6082 at different tube voltages (i.e., different tube spectra). We then regarded the grey values displayed by these projections as a data regression respectively an optimisation problem and obtained the sensitivity curve that is best able to reproduce the measured behaviour in aRTist 2. The resulting sensitivity curve does not necessarily hold physical meaning but is able to simulate the real system behaviour in the simulation software.
The second approach presented in this contribution is to estimate the sensitivity curve based on assumptions about the characteristics of the scintillation detector (e.g., scintillator material, scintillator thickness and signal processing characteristics). For this approach, a linear response function (linear relationship between the deposited energy per pixel and the resulting grey value) is assumed. If the detector characteristics, which affect the simulated deposited energy, are properly modelled, the slope and offset of the response function to match the measured grey values should be the same for different tube spectra. As the offset is constant and given by the grey values measured at no incident radiation, the slope is the remaining parameter to evaluate the success of the detector modelling. We therefore adapted the detector characteristics by changing the detector setup until the slope was nearly the same for all measured tube spectra. We are aware that the resulting parameters of the scintillator material and thickness might not be the real ones, but with those modelling parameters we are able to simulate realistic grey values in aRTist 2. Both of those approaches could potentially be a step forward to a full systematic approach for a digital twin of a real CT system in aRTist 2.
This work presents the use of limited experimental measurements to develop a set of calibrated Simulation parameters that can then be used for reliable simulation of subsea pipeline inspections. The modelling software aRTist is used as the simulation tool, and the calibration is through comparison with experimental images of a well characterised sample in a water tank. Image Quality parameters such as signal-to-noise ratio, contrast and basic spatial resolution are compared with the aim of matching simulated values to experimental results. Currently the model is partially calibrated, with signal-to-noise ratio successfully matched while differences are still found in contrast-to-noise ratio comparisons. This means that measurements depending on absolute intensity are not accurate enough in the simulation at this stage. However, the simulation is found to be accurate for wall thickness measurements in tangential images, which are not based on absolute intensity, with simulated and experimental cases producing similar results.