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Wood-based composites hold the promise of sustainable construction. Understanding the influence on wood cellular microstructure in the macroscopic mechanical behavior is key for engineering highperformance composites. In this work, we report a novel Individual Cell Tracking (ICT) approach for in-situ quantification of nanometer-scale deformations of individual wood cells during mechanical loading of macroscopic millimeter-scale wood samples. Softwood samples containing > 104 cells were subjected to controlled radial tensile and longitudinal compressive load in a synchrotron radiation micro-computed tomography (SRμCT) setup. Tracheid and wood ray cells were automatically segmented, and their geometric variations were tracked during load. Finally, interactions between microstructure deformations (lumen geometry, cell wall thickness), cellular arrangement (annual growth rings, anisotropy, wood ray presence) with the macroscopic deformation response were investigated. The results provide cellular insight into macroscopic relations, such as anisotropic Poisson effects, and allow direct observation of previously suspected wood ray reinforcing effects.
The method is also appropriate for investigation of non-linear deformation effects, such as buckling and deformation recovery after failure, and gives insight into less studied aspects, such as changes in lumen diameter and cell wall thickness during uniaxial load. ICT provides an experimental tool for direct validation of hierarchical mechanical models on real biological composites.
Detection of Multiple Cracks in Four-Point Bending Tests Using the Coda Wave Interferometry Method
(2020)
The enlargement of the cracks outside the permitted dimension is one of the main causes for the reduction of service life of Reinforced Concrete (RC) structures. Cracks can develop due to many causes such as dynamic or static load. When tensile stress exceeds the tensile strength of RC, cracks appear. Traditional techniques have limitations in early stage damage detection and localisation, especially on large-scale structures. The ultrasonic Coda Wave Interferometry (CWI) method using diffuse waves is one of the most promising methods to detect subtle changes in heterogeneous materials, such as concrete. In this paper, the assessment of the CWI method applied for multiple cracks opening detection on two specimens based on four-point bending test is presented. Both beams were monitored using a limited number of embedded Ultrasonic (US) transducers as well as other transducers and techniques (e.g., Digital Image Correlation (DIC), LVDT sensors, strain gauges, and Fiber Optics Sensor (FOS)). Results show that strain change and crack formation are successfully and efficiently detected by CWI method even earlier than by the other techniques. The CWI technique using embedded US transducers is undoubtedly a feasible, efficient, and promising method for long-term monitoring on real infrastructure.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.
Copper sulfide shows some unique physico-chemical properties that make it appealing as a cathode active material (CAM) for solid-state batteries (SSBs). The most peculiar feature of the electrode reaction is the reversible formation of μm-sized Cu crystals during cycling, despite its large theoretical volume change (75%). Here, the dynamic microstructural evolution of CuS cathodes in SSBs is studied using in situ synchrotron X-ray tomography. The formation of μm-sized Cu within the CAM particles can be clearly followed. This process is accompanied by crack formation that can be prevented by increasing the stack pressure from 26 to 40 MPa. Both the Cu inclusions and cracks show a preferential orientation perpendicular to the cell stack pressure, which can be a result of a z-oriented expansion of the CAM particles during lithiation. In addition, cycling leads to a z-oriented reversible displacement of the cathode
pellet, which is linked to the plating/stripping of the Li counter electrode. The pronounced structural changes cause pressure changes of up to 6 MPa within the cell, as determined by operando stack pressure measurements. Reasons for the reversibility of the electrode reaction are discussed and are attributed to the favorable combination of soft materials.
Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.
For dangerous goods packagings, drop testing onto an essential unyielding target can be used to assess the mechanical resistance to impact loads. Adopted regulations like ADR/RID require that the impact surface provided shall be integral with a mass at least 50 times than that of the heaviest package to be tested. The problem is that many manufacturers do not possess impact targets that satisfy the required 50 times mass ratio for regulative drop tests during series production. The objective of this work is to verify existing and define improved criteria for impact target structures based on systematic investigations. Previous evidence highlights the relevance of other parameters in addition to the mass ratio. Therefore, in this research, a variation of drop test parameters was carried out experimentally. Furthermore, numerical vibration analysis was applied to investigate the deformability of the impact surface. The results conclude that the mass ratio of 1:50 cannot be defined as a decisive criterion. In order to determine the influence of further drop test parameters, the research findings were used to validate a parametric model which assesses impact target deflection. An approximation quality of over 90 % was achieved. As a result, new evaluation criteria are proposed. Firstly, a method for identifying critical impact target designs is provided. Secondly, a new comprehensive formula compares the approximated maximum deflection of a real impact target to the respective theoretical threshold derived from a worst-case assumption. In practice, this leads to great advantages in the evaluation of already installed impact targets for dangerous goods packagings.
The microstructure of an apatite-wollastonite (code name AP40) glass-ceramic is analyzed in this study by combining 2D microscopy, phase analysis, X-ray absorption and synchrotron X-ray refraction computed tomography (XCT and SXRCT, respectively). It is shown that this combination provides a useful toolbox to characterize the global microstructure in a wide scale range, from sub-micrometer to millimeter. The material displays a complex microstructure comprising a glassy matrix with embedded fluorapatite and wollastonite small crystals. In this matrix, large (up to 200 μm) spike-shaped structures are distributed. Such microstructural features are oriented around a central sphere, thereby forming a structure resembling a sea urchin. A unique feature of SXRCT, in contrast to XCT, is that internal interfaces are visualized; this allows one to show the 3D distribution of these urchins with exceptionally good contrast. Furthermore, it is revealed that the spike-shaped structures are not single crystals, but rather composed of sub-micrometric crystals, which are identified as fluorapatite and diopside phases by SEM-EDX analysis.
The corner echo is a well-known effect in ultrasonic testing, which allows detection of surface breaking cracks with predominantly perpendicular orientation to the surface as, for example, corrosion cracks in metal pipes or shafts. This echo is formed by two planes, the surface of the crack and the surface which the crack breaks. It can also be classified as a half-skip method, since a reflection of the pulse occurs on the backwall before the reflection at the defect takes place. In combination with the diffraction from the crack tip, the corner echo also allows crack sizing. As shown in this paper, the corner reflection can be used in civil engineering for nondestructive inspection of concrete. Commercially available low frequency ultrasonic arrays with dry point contact sources generate SH transversal waves with sufficient divergence of the sound field in order to detect corner reflections. Ultrasonic line-scans and area-scans were acquired with a linear array on flat concrete specimens, and the data were reconstructed by the Synthetic aperture focusing technique. If the angles and the area of reconstruction are chosen accordingly, the corner echo reflection can be distinguished from other ultrasonic information. The corner echo can thus be used as a method for deciding whether a crack is a partial-depth crack or a full-depth crack and thus for obtaining a statement about crack depth. This paper presents corresponding experimental results obtained on concrete specimens with artificial test defects and cracks induced under controlled conditions.
Fused filament fabrication (FFF) is a material extrusion-based technique often used in desktop 3D printers. Polymeric filaments are melted and are extruded through a heated nozzle to form a 3D object in layers. The extruder temperature is therefore a key parameter for a successful print job but also one of the main emission driving factors as harmful pollutants (e.g., ultrafine particles) are formed by thermal polymer degradation. The awareness of potential health risks has increased the number of emission studies in the past years. However, studies usually refer their calculated emission data to the printer set extruder temperature for comparison purposes. In this study, we used a thermocouple and an infrared camera to measure the actual extruder temperature and found significant temperature deviations to the displayed set temperature among printer models. Our result shows that printing the same filament feedstocks with three different printer models and with identical printer set temperature resulted in a variation in particle emission of around two orders of magnitude. A temperature adjustment has reduced the variation to approx. one order of magnitude. Thus, it is necessary to refer the measured emission data to the actual extruder temperature as it poses a more accurate comparison parameter for evaluation of the indoor air quality in user scenarios or for health risk assessments.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.