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    Structural health monitoring of composites by combining machine learning and synthetic evaluation methods with vibro-acoustic modulations
    (2025)
    Boll, Benjamin
    ;
    Meißner, Robert
    The vibro-acoustic modulation (VAM) method is known for its high sensitivity to detect even small damages and is applied non-destructively. VAM uses a high-frequency ultrasonic carrier wave, that is modulated in the specimen by a high-amplitude pump wave of significantly lower frequency. The method was introduced in the 1990s, and the literature today includes over 200 journal publications in which VAM is applied to many laboratory-based applications. However, no industrial application has yet been proclaimed. Since transitioning from the laboratory to the industry is one of the biggest hurdles, this thesis aims to address and overcome the limitations to enable the next step toward industrial applications. The requirements on the method depend on whether it will be used in the context of non-destructive testing (NDT) or the structural health monitoring (SHM). VAM, as traditionally described in the literature, has no defined baseline of the measurement. Hence, measurements of different specimens can not be reliably compared. In SHM applications, the first measurement is usually assumed to be pristine and is utilised as this baseline. Despite current investigations into estimating a baseline, it remains a major challenge (due to complex signal modulation and dependencies on the material, preexisting damage, \textit{etc.}). To overcome this issue---specifically for NDT applications---this thesis proposes a data-driven approach that is validated with two applications. First, the adhesive bonding of fibre composite structures is investigated. Single-lap shear specimens with so-called weak bonds and kissing bonds were prepared by inserting a non-stick film, or by contaminating the bond line with a release agent. It is shown that a data-driven evaluation can accurately differentiate between undamaged and damaged specimens in specific frequency ranges, even though the differences based on the traditional evaluation methods are minimal. The trained neural networks are evaluated to recursively generate information on the significance of the input values, leading to a deeper understanding of the VAM method and its mechanisms. The applicability of this data-driven evaluation is confirmed by testing welded steel specimens, where half of them contained crystallisation defects that resulted from false welding parameters. When VAM is applied as an SHM method, it is advantageous to leverage the ambient vibrations as the modulating pump wave. Ideally, SHM systems are built with low-power and energy-harvesting devices to reduce installation and maintenance costs. The challenge results from variations of the ambient vibration due to changes of environmental influences. Thus, the traditional VAM measurements would neither be consistent nor comparable between the measurements over time. This challenge is overcome by the proposed synthetic computation of the VAM signal. This synthetic computation minimises the dependency on the ambient vibration so that the VAM measurement can be performed on complex components without interference from environmental changes. The presented approach significantly reduces the requirements on the sensor nodes in terms of sampling rate, measured data points, data size, and energy required to drive the high-frequency emitter. Thus, the usage of self-sustaining energy-harvesting sensor nodes comes into reach. Furthermore, the synthetic method can be implemented into most existing SHM systems that contain acoustic emissions or guided wave measurements with minimal adaptations. Finally, the viability of the synthetic VAM method is demonstrated on larger and more complex structures.
    Publicationtype: Doctoral Thesis
      77  75
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    Dynamic structure investigation and spectra prediction of biomolecules using machine learning techniques
    (2024)
    Kotobi, Amir
    ;
    Meißner, Robert
    The investigation of biomolecular structures and the prediction of their spectra using experimental and theoretical studies in the gas phase represent fundamental steps in comprehending their intrinsic properties and biological functions. Nonetheless, the complexity of the potential energy surface of biomolecules, combined with limitations in computational resources, limits the interpretation of experimental observations. Integrating supervised and unsupervised machine learning (ML) techniques into theoretical calculations is considered as an effective way to address these challenges. Infrared (IR) and X-ray absorption spectroscopy (XAS) has proven to be powerful experimental techniques to study the electronic and spatial structure of biomolecules such as peptides and proteins. Reproducing and validating the features observed in spectra resulting from these experiments often requires the use of sophisticated ab initio calculations and comprehensive understanding of biomolecules’ configurational space. In this thesis, I introduced a novel approach in interpretation of IR experimental spectrum of a peptide which aims enhancing the exploratory power of searching configurational space by combining REMD simulations, unsupervised machine learning, and ab initio calculations. This scheme relies on a set of structural descriptors and data-driven clustering technique which accounts for canonical ensemble of real experimental condition to obtain an accurate computed spectrum. We show that by partitioning the configurational space into subensembles of imilar conformations i.e. clusters, an accurate IR spectrum can be calculated by averaging the IR contribution of each representative conformer in each cluster, weighted according to the population of each cluster. While this approach unravels important fingerprints of experimental spectroscopic data, the calculation of IR and particularly XAS spectra, due to its inherently expensive theoretical computation, is often computationally prohibitive task for even medium-sized molecules. To remedy the computational obstacles associated with spectra prediction, we develope a data-driven supervised ML frameworks, i.e. graph neural networks which are trained on a custom-generated XAS dataset to find a mapping between structures and spectroscopic signals, thus bypassing the need for expensive ab initio quantum chemistry calculations. To insure the interpretability of GNN models’ predictions, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. Within this approach, we show that it is possible to link the peaks observed in the spectra to certain core and virtual orbitals from the quantum chemical calculations and obtain an in-depth understanding of the ML predicted XAS spectrum. The results presented in this thesis show that the integration of supervised and unsupervised ML techniques can effectively enhance the interpretation of spectroscopic data and make efficient use of the expensive ab initio calculations.
    Publicationtype: Doctoral Thesis
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    A computational approach to magnesium corrosion engineering
    (Dr. Hut, 2023)
    Würger, Tim
    ;
    Meißner, Robert
    Magnesium is the lightest metallic construction material and bears high potential for automotive components, medical implants and energy storage systems. However, the practical use of untreated magnesium alloys is restricted as they are prone to corrosion. An essential prerequisite for the inhibition or control of the degradation process is a deeper understanding of the underlying corrosion mechanisms. For instance, identifying the reaction pathways most relevant for the release of gaseous hydrogen—one of the main products during magnesium corrosion—builds the foundation for adapting the hydrogen evolution rate in general. A possible approach to modulate the hydrogen evolution and magnesium dissolution rate is the introduction of small organic molecules that either stabilize the corroding surface or capture corrosive species. Such molecules constitute benign and useful materials to modify the service environment of magnesium for applications that require tailored degradation properties. This thesis illuminates different aspects of magnesium degradation, ranging from atomistic insights into the corrosion process itself to further developing strategies for effective corrosion control. By combining atomistic simulations and machine learning techniques, a holistic framework is developed to improve understanding and enable more efficient magnesium corrosion engineering in the future.
    Publicationtype: Doctoral Thesis
      440  1736
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    Krümmungs-Kapazitäts-Beziehungen gewellter Elektroden in elektrochemischen Doppelschichtkondensatoren
    (2023)
    Seebeck, Jannes
    ;
    Meißner, Robert
    Like capacitors, the storage mechanism of electrochemical double-layer capacitors is based on the formation of electrostatic fields between the electrodes and a double layer formed by the ions in the electrode-electrolyte interface. Beside cycle stability and the charging and discharging speed the performance is also characterized by the capacity of the electrodes. However the capaitance is strongly influenced by the size of the electrode surface on which it is possible for the electrolyte ions to accumulate in the form of a double layer. Due to their high surface area, amorphous carbons are considered typical electrode materials for common supercapacitors. Among others, these electrodes are characterized by convex and concave curvatures, among other things. However, the interaction of convex and concave regions and their superposition have rarely been investigated. In addition this work demonstrates a large impact of nanoscale surface curvatures, either convex or concave, on the capacitive performance of supercapacitors. Molecular dynamics simulations of trilayer graphene electrodes with convex and concave curved regions of different curvature radii are performed to describe this interaction in more detail. A 1.5 M solution of the ionic liquid 1-butyl-3-methylimidazoliumhexafluorophosphate [BMI][PF6] and acetonitil as solvent serves as the electrolyte. The definition of constant potentials at the electrodes allows the determination of the charge of each electrode atom depending on the time-dependent electrolyte configuration. The results show a variable behavior of the differential capacitance with respect to different curvature radii both for the overall electrode and when the concave and convex regions were plotted separately. Furthermore, this work describes the influence of the electrode curvature on the orientation of the cations adsorbed on the electrode surface and the composition of the ionic layer accumulated in the electrode-electrolyte-interface and correlates this influence with the differential capacitance peaks. The evaluation of more exhaustive simulations of selected systems by using the Unbinned Weighted Histogram Analysis Method (UWHAM) provides a higher statistical accuracy and allows the results to be presented as a continuous function of the applied potential. These calculations also reveals potential-dependent fluctuations of the differential capacitance, the compostion of the ionic layer and the cation orientation. If the curvature radii are of the same order of magnitude as the ions, the restricted space in the electrode-electrolyte interface causes a reduced number of possibilities for the orientation of the ions within this interface. This leads to changes in free energy compared to flat electrodes or electrodes with significantly larger curvature radii. Furthermore, the capacitance peaks of the negative electrode can be related not only to variations in the composition of the Helmholtz layer of the negative electrode, but also to variations at the positive electrode. It is found that both the applied potential and the electrode curvature determine the occurrence of capacitive peaks in the convex and concave regions and thus determine the capacitive performance of the electrode. Furthermore, by calculating a charge-charge covariance matrix from the charge fluctuations of the electrode atoms, the influence of individual atomic electrode charges or defined regions of the electrode on the distinct nontrivial features of the differential capacitance can be quantified in a sophisticated manner. This method can also be applied to more complex electrode models with different pore geometries, thus showing the influence of different morphological characteristics on the capacitance. For this reason, the method presented here can serve as a tool in defining design criteria of future electrodes.
    Publicationtype: Doctoral Thesis
      70  62