8.4 Akustische und elektromagnetische Verfahren
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- 8.4 Akustische und elektromagnetische Verfahren (365)
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Eingeladener Vortrag
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Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen.
The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs).
The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV.
Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings.
Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility. The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.
Many modern ultrasonic methods in the fields of Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) require simulations in research. Researchers either use simulation data initially during development to investigate certain aspects, or the simulation process is directly part of the research task. Examples of the second case are inverse methods for parameter estimation, model-assisted probability of detection analysis or the generation of training data for AI algorithms. All these applications require algorithms that are as efficient as possible. For methods based on explicit time-step methods, a significant increase in efficiency can be achieved if a so-called lumped mass matrix can be used, which approximates the consistent mass matrix but is easier to invert.
The finite element method has been the subject of many studies on approximations of the mass matrix. In contrast, the lumped mass matrix in the context of the Scaled Boundary Finite Element Method (SBFEM) is a current field of research [1,2]. In the time domain, the semi-analytical SBFEM is notable for its flexibility to be applied to polygonal meshes. In particular, image-based mesh generation using a quadtree algorithm is possible. In general, polygonal meshes have the same flexibility as triangular meshes, but polygonal meshes can have additional advantages such as greater tolerance to distortion.
In this contribution, the SBFEM formulation based on bubble functions [3] for the time domain is presented for two-dimensional elastic waves. The adjustments necessary for a good approximating lumped mass matrix are emphasized. Several grid generation methods for polygonal elements are shown. Figure 1 depicts the difference between the consistent mass matrix and the lumped mass matrix for a normal polygonal mesh. Finally, the accuracy of mass lumping for linear, quadratic and cubic shape functions is presented and the computational efficiency is demonstrated using exemplary waveguide geometries.
Hydrogen is an energy source of increasing importance. As hydrogen is very reactive to air and needs to be stored under high pressure, it is crucial to provide safe transportation and storage. Therefore, structural health monitoring, based on guided ultrasonic waves and machine learning methods, is used for Composite Overwrapped Pressure Vessels (COPVs) containing hydrogen. To acquire data that allows robust detection of COPV defects, there are two main process parameters to consider. These are the pressurization of the vessel and the temperature conditions at the vessel. This paper will focus on the derivation of a design of experiment (DoE) from the needs of various validation scenarios (e.g. concerning pressure, temperature or excitation frequency). Practical limitations must be considered as well. We designed experiments with multiple reversible damages at different positions. A network of 25 transducers, structured as five rings with five sensors in one line, is installed on a vessel. Guided ultrasonic waves are used via the pitch-catch procedure, which means that the transducers act pairwise as transmitter and receiver in order to measure all transmitterreceiver combinations. This leads to 600 signal paths, recorded by a Verasonics Vantage 64 LF data acquisition system. Finally, the influences of temperature and pressure within the acquired data set are going to be visualized.
Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.
Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure.
The main advantage of air-coupled ultrasonic testing is the absence of a liquid couplant, which can damage some materials. However, most air-coupled testing scenarios have the challenge of low signals and a signal-to-noise ratio (SNR) several orders of magnitude lower than with couplant-assisted techniques. Since this challenge of small SNR also exists in radar technology, the pulse compression used there was adapted and applied to the physical conditions of air-coupled ultrasonic testing. This paper presents ultrasonic transmission measurements on a carbon-fibre-reinforced polymer plate using two experimental setups: 1) a thermoacoustic transmitter and an optical microphone and 2) a pair of ferroelectret transducers as transmitter and receiver. Thermoacoustic transmitters convert electrical energy to heat, which causes the air to expand thus producing acoustic waves. The optical microphone is based on a Fabry-Perot interferometer. Ferroelectrets are charged cellular polymers, having piezoelectric properties and excellent acoustic matching to air. Both thermoacoustic transmitters and ferroelectrets are non-linear regarding the relationship between the excited sound pressure and the excitation voltage. Due to these physical boundary conditions, unipolar coding was used to modulate the excitation signals. Various codes were tested, and parameters of the excitation pulses were varied to find the optimal combination for each experimental setup. The application of pulse compression to the combination of thermoacoustic transmitter and optical microphone increased the signal-to-noise ratio by up to 16 dB and for the ferroelectret transducers by up to 23 dB.
The main advantage of air-coupled ultrasonic testing is the absence of a liquid couplant, which can damage some materials. However, most air-coupled testing scenarios have the challenge of low signals and a signal-to-noise ratio (SNR) several orders of magnitude lower than with couplant-assisted tech-niques. Since this challenge of small SNR also exists in radar technology, the pulse compression used there was adapted and applied to the physical conditions of air-coupled ultrasonic testing. This paper presents ultrasonic transmission measurements on a carbon-fibre-reinforced polymer plate using two experimental setups: 1) a thermoacoustic transmitter and an optical microphone and 2) a pair of ferroe-lectret transducers as transmitter and receiver. Thermoacoustic transmitters convert electrical energy to heat, which causes the air to expand thus producing acoustic waves. The optical microphone is based on a Fabry-Perot interferometer. Ferroelectrets are charged cellular polymers, having piezoelectric proper-ties and excellent acoustic matching to air. Both thermoacoustic transmitters and ferroelectrets are non-linear regarding the relationship between the excited sound pressure and the excitation voltage. Due to these physical boundary conditions, unipolar coding was used to modulate the excitation signals. Vari-ous codes were tested, and parameters of the excitation pulses were varied to find the optimal combina-tion for each experimental setup. The application of pulse compression to the combination of thermo-acoustic transmitter and optical microphone increased the signal-to-noise ratio by up to 16 dB and for the ferroelectret transducers by up to 23 dB.
How structural health monitoring can be embedded in a digital quality infrastructure: an example.
(2024)
The digital Quality Infrastructure (QI) initiative “QI-Digital” in Germany is focusing on implementing new technologies and approaches to ensure that the task of quality assurance is more efficient and ready for the digital and green transformation of the economy. The implementation of quality control key elements, such as Smart Standards, Digital Certificates and QI-cloud solutions shall contribute to solving the socio-economic, ecological, and technological challenges of our time. Hydrogen is a key energy carrier and has the potential to play a significant role in the energy transition, especially in mobility. An essential factor for the broad acceptance of hydrogen-based mobility is the availability of refueling stations that operate reliably and safely. Using the example of a Hydrogen Refueling Station (HRS) built within the QI-Digital initiative, the Federal Institute for Material Research and Testing (BAM) aims to establish a real laboratory where modern measurement techniques and new digital methods are implemented to enhance operational safety, availability, and economic efficiency and render the technology more attractive for the industry.
In this work, we present an approach to establish a Structural Health Monitoring (SHM) system on a high-pressure buffer inside HRS and show how it could be embedded into a digital QI. The high-pressure buffers are essential components of the plant which are currently inspected periodically without regard to their operating history. Focusing on the transition to a continuous and digitally supported monitoring of the component’s integrity during operation the novel inspection scheme will be linked to a completely digitalized component-related documentation and tested using digital certificates. This allows the operational safety and, if necessary, the remaining useful lifetime to be assessed on an ongoing basis and to be a valuable contribution to increasing sustainability.
This article presents a method to use the dispersive behavior of ultrasonic guided waves and neural networks to determine the isotropic elastic constants of plate-like structures through dispersion images. Therefore, two different architectures are compared: one using convolutions and transfer learning based on the EfficientNetB7 and a Vision Transformer-like approach. To accomplish this, simulated and measured dispersion images are generated, where the first is applied to design, train, and validate and the second to test the neural networks. During the training of the neural networks, distinct data augmentation layers are employed to introduce artifacts appearing in measurement data into the simulated data. The neural networks can extrapolate from simulated to measured data using these layers. The trained neural networks are assessed using dispersion images from seven known material samples. Multiple variations of the measured dispersion images are tested to guarantee the prediction stability. The study demonstrates that neural networks can learn to predict the isotropic elastic constants from measured dispersion images using only simulated dispersion images for training and validation without needing an initial guess or manual feature extraction, independent of the measurement setup. Furthermore, the suitability of the different architectures for generating information from dispersion images in general is discussed.