Chemie und Prozesstechnik
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- 2022 (192) (entfernen)
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- Non-destructive testing (17)
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- Air-coupled ultrasound (10)
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Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (192) (entfernen)
Eingeladener Vortrag
- nein (70)
In general, wind turbines transform the kinetic energy of the wind into electric power. Thereby, the wind turbine blades are facing unsteady loads which are transferred to the hub to generate a rotation of the turbine’s axis. This brief introduction focuses on the aerodynamics of the blades and the corresponding loads. Starting with the basic flow field and loads of an airfoil, terms like stagnation point, boundary layer, Reynolds number, transition, and separation are introduced. For different geometries, lift and drag coefficient curves are discussed. Then, full wings will be considered, including their three-dimensional flow field due to wing tip vortices and crossflows. As a main source of increased loads, unsteady effects are explained in more detail such as gusts, tower passing, earth boundary layer crossing, free stream turbulences, yaw misalignment, etc. At the end, extra loads due to an oscillating free stream are introduced.
Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
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.
Die zerstörungsfreie Prüfung von metallischen Bauteilen hergestellt mit additiver Fertigung (Additive Manufacturing - AM) gewinnt zunehmend an industrieller Bedeutung. Grund dafür ist die Feststellung von Qualität, Reproduzierbarkeit und damit auch Sicherheit für Bauteile, die mittels AM gefertigt wurden. Jedoch wird noch immer ex-situ geprüft, wobei Defekte (z.B. Poren, Risse etc.) erst nach Prozessabschluss entdeckt werden. Übersteigen Anzahl und/oder Abmessung die vorgegebenen Grenzwerte für diese Defekte, so kommt es zu Ausschuss, was angesichts sehr langer Bauprozessdauern äußerst unrentabel ist. Eine Schwierigkeit ist dabei, dass manche Defekte sich erst zeitverzögert zum eigentlichen Materialauftrag bilden, z.B. durch thermische Spannungen oder Schmelzbadaktivitäten. Dementsprechend sind reine Monitoringansätze zur Detektion ggf. nicht ausreichend.
Daher wird in dieser Arbeit ein Verfahren zur aktiven Thermografie an dem AM-Prozess Laser Powder Bed Fusion (L-PBF) untersucht. Das Bauteil wird mit Hilfe des defokussierten Prozesslasers bei geringer Laserleistung zwischen den einzelnen gefertigten Lagen unabhängig vom eigentlichen Bauprozess erwärmt. Die entstehende Wärmesignatur wird ort- und zeitaufgelöst durch eine Infrarotkamera erfasst. Durch diese der Lagenfertigung nachgelagerte Prüfung werden auch zum Bauprozess zeitversetzte Defektbildungen nachweisbar.
In dieser Arbeit finden die Untersuchungen als Proof-of-Concept, losgelöst vom AM-Prozess, an einem typischen metallischen Testkörper statt. Dieser besitzt eine Nut als oberflächlichen Defekt. Die durchgeführten Messungen finden an einer eigens entwickelten L-PBF-Forschungsanlage innerhalb der Prozesskammer statt. Damit wird ein neuartiger Ansatz zur aktiven Thermografie für L-PBF erforscht, der eine größere Bandbreite an Defektarten auffindbar macht. Der Ansatz wird validiert und Genauigkeit sowie Auflösungsvermögen geprüft. Eine Anwendung am AM-Prozess wird damit direkt forciert und die dafür benötigten Zusammenhänge werden präsentiert.
Die zerstörungsfreie Prüfung von metallischen Bauteilen hergestellt mit additiver Fertigung (Additive Manufacturing - AM) gewinnt zunehmend an industrieller Bedeutung. Grund dafür ist die Feststellung von Qualität, Reproduzierbarkeit und damit auch Sicherheit für Bauteile, die mittels AM gefertigt wurden. Jedoch wird noch immer ex-situ geprüft, wobei Defekte (z.B. Poren, Risse etc.) erst nach Prozessabschluss entdeckt werden. Übersteigen Anzahl und/oder Abmessung die vorgegebenen Grenzwerte für diese Defekte, so kommt es zu Ausschuss, was angesichts sehr langer Bauprozessdauern äußerst unrentabel ist. Eine Schwierigkeit ist dabei, dass manche Defekte sich erst zeitverzögert zum eigentlichen Materialauftrag bilden, z.B. durch thermische Spannungen oder Schmelzbadaktivitäten. Dementsprechend sind reine Monitoringansätze zur Detektion ggf. nicht ausreichend.
Daher wird in dieser Arbeit ein Verfahren zur aktiven Thermografie an dem AM-Prozess Laser Powder Bed Fusion (L-PBF) untersucht. Das Bauteil wird mit Hilfe des defokussierten Prozesslasers bei geringer Laserleistung zwischen den einzelnen gefertigten Lagen unabhängig vom eigentlichen Bauprozess erwärmt. Die entstehende Wärmesignatur wird ort- und zeitaufgelöst durch eine Infrarotkamera erfasst. Durch diese der Lagenfertigung nachgelagerte Prüfung werden auch zum Bauprozess zeitversetzte Defektbildungen nachweisbar.
In dieser Arbeit finden die Untersuchungen als Proof-of-Concept, losgelöst vom AM-Prozess, an einem typischen metallischen Testkörper statt. Dieser besitzt eine Nut als oberflächlichen Defekt. Die durchgeführten Messungen finden an einer eigens entwickelten L-PBF-Forschungsanlage innerhalb der Prozesskammer statt. Damit wird ein neuartiger Ansatz zur aktiven Thermografie für L-PBF erforscht, der eine größere Bandbreite an Defektarten auffindbar macht. Der Ansatz wird validiert und Genauigkeit sowie Auflösungsvermögen geprüft. Eine Anwendung am AM-Prozess wird damit direkt forciert und die dafür benötigten Zusammenhänge werden präsentiert.
Aktuelles aus der Normung
(2022)
Damage detection of reinforced concrete (RC) structures is becoming a more attractive domain due to the safety issues arising in the last few decades. The damage in concrete can be caused by excessive exploitation of the structure or environmental effects. The cracks in concrete can be detected by different nondestructive testing methods. However, the available methods used for this purpose have numerous limitations. The technologies available in the market nowadays have difficulties detecting slowly progressive, locally limited damage. In addition, some of These methods cannot be applied, especially in hard-to-reach areas in the superstructures. In order to avoid these deficiencies, an embedded ultrasonic methodology can be used to detect cracks in RC structures. In this study, the methodology of crack detection supported with the advanced Signal processing algorithm was proposed and verified on RC structures of various types, and cracks occurring between embedded sensors can be detected. Moreover, different pairs of ultrasonic sensors located in the considered structures are used for the analysis of the sensitivity of distance between them. It is shown that the ultrasonic sensors placed in the range of 1.5–2 m can detect cracks, even when the other methods failed to detect changes in the structure. The obtained results confirmed that diffuse ultrasonic sensor methodology is able to monitor real structures more effectively than traditional techniques.
The main application of LIBS in civil engineering is the detection of harmful ions in concrete, which can penetrate the component through the porous concrete structure. The advantages of LIBS over standard methods are the possibility of multi-element analysis, measurement speed, spatially resolved measurements, and minimal sample preparation. The spatially resolved measurements of LIBS allow the assessment of the heterogeneity of the concrete by measuring separately the chemical composition of the aggregates and the binder matrix. The latter is particularly relevant because the determined elemental distribution can be directly related to the binder matrix. This is not possible with standard methods, since the material is homogenized to powder during sample preparation stage and the determined concentration is thus related to the total mass. In addition to the use of LIBS for the specific analysis of individual harmful ions, LIBS can also be used to estimate the concrete composition and thus determine, for example, the type of cement used. Corresponding information are relevant for the estimation of the remaining service life and for the preparation of a maintenance concept. In recent years, LIBS has been increasingly used in civil engineering. Currently, however, it is primarily used in research institutions and only occasionally in building materials laboratories. Special commercial devices have also been developed, which greatly simplify the application due to the high degree of automation. Mobile LIBS systems allow on-site application. A central point, which limits the use of LIBS in the commercial sector, is the lack of norms and standards. Therefore, within the framework of a project funded by the German government, work has been carried out on the preparation of a leaflet on quantitative chlorine determination in concrete, which will be published this year. In interlaboratory comparisons the robustness and accuracy for the practical application was demonstrated. LIBS also has great potential in the recycling of construction waste in conjunction with hyperspectral sensors. This issue is currently being addressed in a national project. During the presentation, the state of the art of LIBS in civil engineering will be presented, next steps will be discussed, and future challenges will be outlined.