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Virtual CT acquisition and reconstruction of complex and noisy scanning trajectories in aRTist
(2019)
In modern CT imaging, simulation has become an important tool to minimize cost- and time-intensive measurements. It is increasingly used to optimize techniques for complex applications, to support the preparation of written procedures, and for educational purposes. We extended the CT simulation software ‘aRTist’ with a module to set-up arbitrary trajectories including disturbing influences during the scan. Moreover, such geometric deviations can be compensated by the internal reconstruction tool.
Virtual CT with aRTist
(2023)
The software aRTist is a simulation tool for the generation of realistic radiographs of virtual radiographic superstructures.
With radiographic simulations, virtual component models can be scanned as in a computer tomograph.
Industrial X-ray computed tomography (CT) enables the non-destructive detection of internal and external surfaces as well as inhomogeneities of technical objects. Virtual CT offers new possibilities for the investigation of parameter influences of this complex testing and measuring technique. In addition to the option of switching physical effects on and off, scanning movements can also be tested before their technical realization.
The virtual CT generates projection images from different directions for the subsequent reconstruction of a volume model of the examined object. The reconstruction of the simulated scans is carried out with the algorithms and programs for real scans. Tomographic scans consist of a large number of projections, which practically cannot be generated individually by the user of a simulation. The software offers various options for the automated simulation of tomographic scans. These range from standard CT to scans on free trajectories or with individual projection matrices.
The project series CTSimU was initiated with the goal to develop a set of procedures to enable the determination of the task-specific measurement uncertainty of a CT system numerically by radiographic simulation. The first project (2019-2022) “Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation - CTSimU” was focused on the sufficient physical correctness of the radiographic simulation and created as a result a test framework for simulation softwares and a draft of a VDI standard in the series VDI/VDE 2630 for this application. However, for the realistic simulation of a CT system in a simulation software (i.e. a digital twin), not only the correctness of the simulation software itself is crucial, but also the quality of the parameterization of the CT system in the simulation software - this represents the starting point of the 2nd project “Realistic Simulation of real CT systems with a basic-qualified Simulation Software - CTSimU2” (2022-2024).
The parameterization of a CT system in a simulation software can be divided into four steps: after the data acquisition at the real CT system (step 1) follows the evaluation of the acquired data for the generation of general parameter specifications (step 2). It follows the transfer of the parameters into the specific simulation software (step 3) and the validation of the resulting simulation parameters by a suitable test (step 4). The intended result of the project CTSimU2 is a draft VDI standard (for VDI/VDE 2630) for this test, which contains an informative annex on the state of the art regarding the possibilities for parameter determination.
Radiografische Simulationswerkzeuge wie aRTist, ScorpiusXLab, SimCT oder CIVA CT verwenden analytische Methoden und physikalische Monte-Carlo-Teilchentransportsimulationen, um die Interaktionsprozesse zwischen Röntgenstrahlung und Materie zu simulieren. Die berechneten Projektionen bilden anschließend unter Berücksichtigung einer definierten Scan-Trajektorie die Basis der Simulation einer röntgencomputertomografischen Untersuchung.
Für den erfolgreichen Einsatz der Computertomografie, sei es als zerstörungsfreie Prüfmethode oder beim dimensionellen Messen, ist es generell notwendig, bekannte Fehler- bzw. Abweichungsquellen des Messverfahrens auszuschließen oder zu reduzieren. Dabei hat sich gezeigt, dass die Auswahl der Messparameter und die Erfahrung des Anwenders direkten Einfluss auf das erzielbare Messergebnis einer computertomografischen Untersuchung nehmen. Es ist daher sinnvoll, die Parameterauswahl in einem virtuellen Simulationsaufbau vorher zu erproben und an die Messaufgabe anzupassen.
Neben der Optimierung von Messparametern finden radiografische Simulationswerkzeuge auch Anwendung für Machbarkeitsstudien und werden zur Schulung von Anwendern im Bereich der Röntgen-Computertomografie verwendet.
Radiografische Simulationswerkzeuge befinden sich in einem stetigen Wandel, beispielsweise durch die Entwicklung neuer Rekonstruktionsmethoden, durch Erweiterung von analytischen Modellen, durch Integration komplexer Trajektorien oder durch Berücksichtigung von prozessbedingten geometrischen Abweichungen. Im laufenden EMPIR-Projekt „AdvanCT“ entsteht deshalb ein „Good Practice Guide“ für die Simulationsumgebung aRTist. Ziel dieses Guides ist es, die mit steigender Komplexität verbundenen Einstiegshürden für Anwender von aRTist zu reduzieren, um damit einen praxisnahen Zugang zur virtuellen Computertomografie zu ermöglichen. Dabei werden anhand von praktisch nachvollziehbaren Beispielen die grundlegenden Mechanismen der Simulationsumgebung erklärt und ein strukturierter Leitfaden zur Simulation röntgencomputertomografischer Untersuchungen mit aRTist vermittelt.
In diesem Beitrag werden erste Auszüge des Guides sowie eine Übersicht der weiteren geplanten Themen für die anschließende Diskussion vorgestellt.
The interest in using computer simulations of dimensional x-ray computed tomography (dXCT) for various metrological purposes—such as measurement planning, performance prediction, performance optimisation and, finally, measurement uncertainty estimation—is increasing along with the ever-growing demand for more reliable measurements with dXCT. However, before a piece of simulation software can be used for tasks related to coordinate metrology, it has to be ensured that it is able to simulate physical laws, characteristic effects and basic CT system functionalities correctly and with sufficient accuracy. In short, the software must be qualified for dimensional metrology tasks. As one part of such a qualification process, a method is presented here for determining conformity intervals of 2D tests (projection-based tests) based on 3D tests (testing based on dimensional evaluations in a reconstructed volume) for the assessment of dXCT simulation software. The method consists of varying relevant parameter values in order to verify their influence on 3D measurement results. The results of the 3D tests with varied parameter values are then transferred to the quantities tested in the 2D tests and used as the basis for determining conformity intervals. Two approaches are applied for determining whether or not a variation of a parameter value is significant: (a) statistical and (b) heuristic. Two examples are presented, each based on simulated images, which show the application of the two different approaches for determining conformity intervals for the results of the 2D tests.
The inspection of complex-shaped components, such as those enabled by additive manufacturing, is a major challenge in industrial quality assurance. A frequently adopted approach to volumetric non-destructive evaluation is X-ray computed tomography, but this has major drawbacks. Two-dimensional radiography can overcome some of these problems, but does not generally provide an inspection that is as capable.
Moreover, designing a detailed inspection for a complex-shaped component is a labour-intensive task, requiring significant expert input. In response, a computational framework for optimizing the data acquisition for an image-based inspection modality has been devised. The initial objective is to advance the capabilities of radiography, but the algorithm is, in principle, also applicable to alternative types of imaging. The algorithm exploits available prior
information about the inspection and simulations of the inspection modality to allow the Determination of the optimal inspection configuration, including specifically component poses with respect to the imaging system. As an intermediate output, spatial maps of inspection performance are computed, for understanding spatially varying limits of detection. Key areas of innovation concern the defect detectability evaluation for arbitrarily complex indications and the creation of an application-specific optimization algorithm. Initial trials of the algorithm are presented, with good results.
Structural Health Monitoring (SHM) mittels geführter Ultraschallwellen ermöglicht eine kontinuierliche Überwachung komplexer Bauteile und liefert umfangreiche Informationen über deren strukturelle Integrität. Die aufgezeichneten Signale sind jedoch komplex und werden daher zunehmend mittels KI verarbeitet, um Anomalien in der Systemintegrität automatisch zu erkennen. Eine Herausforderung dabei ist jedoch das Trainieren solcher KI-Modelle, da dafür große Mengen an Trainingsdaten inklusive diverser Schadensfälle benötigt werden, um eine hohe Genauigkeit und Zuverlässigkeit zu gewährleisten. Experimentelle Generierung dieser Daten ist wegen des enormen Zeit- und Kostenaufwands nicht möglich.
Eine Alternative hierfür bieten die numerischen Simulationen, da dadurch Trainingsdaten für beliebige Anzahl an Schadenskonfigurationen erzeugt werden können. Das Hauptproblem dabei ist jedoch die Abweichung zwischen den experimentellen und simulierten Daten (durch z.B. Abweichungen bei der Geometrie, Randbedingungen oder Materialparameter). Um diese Abweichungen zu reduzieren bzw. zu eliminieren, wird ein KI-Modell mit einer Autoencoder-Decoder Architektur vorgestellt, die mittels eines Experiments kalibriert werden kann und anschließend in der Lage ist, die Genauigkeit der Simulationsergebnisse nachträglich zu erhöhen.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.
Non-destructive testing of rail tracks is carried out by using rail inspection cars equipped with ultrasonic and eddy current measurement. The evaluation of test data is mainly done manually, supported by a software tool which pre-selects relevant indications shown to the evaluators. The resulting indications have to be checked on-site using hand-held testing equipment. Maintenance interventions are then derived on the basis of these on-site findings.
Overall aim of the AIFRI (Artificial Intelligence For Rail Inspection) project - funded by the German Federal Ministry of Digital and Transport (BMDV) as part of the mFUND programme under funding code 19FS2014 – is to increase the degree of automation of the inspection process from the evaluation of the data to the planning of maintenance interventions. The accuracy of defect detection shall be increased by applying AI methods in order to enable an automated classification of detected indications into risk classes. For this purpose, data from both eddy current inspections and ultrasonic inspections will be used in combination.
Within the framework of this data-driven project, relevant defect patterns and artefacts present in the rail are analysed and implemented into a configurable digital twin. With the help of this digital twin virtual defects can be generated and used to train AI algorithms for detection and classification. With the help of reliability assessment trained AI algorithms will be evaluated with regard to the resulting quality in defect detection and characterisation.
A particular aspect of the development of AI methods is the data fusion of different NDT data sources: Thereby, synergies are used that arise from linking eddy current and ultrasonic inspection data in a combined model.
In the course of the project a demonstrator consisting of the developed IT-tool and an asset management system will be implemented and tested in the field using real-world data.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.