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An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
In this work, the thermal properties of ternary Li₃ₓCo₇₋₄ₓSb₂₊ₓO₁₂ solid solutions are studied for different concentrations in the range 0 ≤ x ≤ 0.7. Samples are elaborated at four different sintering temperatures: 1100, 1150, 1200 and 1250 °C. The effect of increasing the content of Li⁺ and Sb⁵⁺, accompanied by the reduction of Co²⁺, on the thermal properties is studied. It is shown that a thermal diffusivity gap, which is more pronounced for low values of x, can be triggered at a certain threshold sintering temperature (around 1150 °C in this study). This effect is explained by the increase of contact area between adjacent grains. Nevertheless, this effect is found to be less pronounced in the thermal conductivity. Moreover, a new framework for heat diffusion in solids is presented that establishes that both the heat flux and the thermal energy (or heat) satisfy a diffusion equation and therefore highlights the importance of thermal diffusivity in transient heat conduction phenomena.
Laser powder bed fusion of metallic components (PBF-LB/M) is gaining acceptance in industry. However, the high costs and lengthy qualification processes required for printed components create the need for more effective in-situ monitoring and testing methods. This article proposes multispectral Optical Tomography (OT) as a new approach for monitoring the PBF-LB/M process. Compared to other methods, OT is a low-cost process monitoring method that uses long-time exposure imaging to observe the build process. However, it lacks time resolution compared to expensive thermographic sensor systems. Monochromatic OT (1C-OT) is already commercially available and observes the building process layer-wise using a single wavelength window in the NIR range. Multispectral OT (nC-OT) utilizes a similar setup but can measure multiple wavelength ranges per location simultaneously. By comparing the classical 1C-OT and nC-OT approaches, this article examines the advantages of nC-OT (two channel OT and RGB-OT) in reducing the false positive rate for process deviations and approximating maximum temperatures for a better comparison between different build processes and materials. This could ultimately reduce costs and time for part qualification. The main goal of this contribution is to assess the advantages of nC-OT compared to 1C-OT for in-situ process monitoring of PBF-LB/M.
LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste
(2023)
AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt.
This data article introduces a dataset comprising 1630 alkali-activated concrete (AAC) mixes, compiled from 106 literature sources. The dataset underwent extensive curation to address feature redundancy, transcription errors, and duplicate data, yielding refined data ready for further data-driven science in the field of AAC, where this effort constitutes a novelty. The carbon footprint associated with each material used in the AAC mixes, as well as the corresponding CO2 footprint of every mix, were approximated using two published articles. Serving as a foundation for future expansions and rigorous data applications, this dataset enables the characterization of AAC properties through machine learning algorithms or as a benchmark for performance comparison among different formulations. In summary, the dataset provides a resource for researchers focusing on AAC and related materials and offers insights into the environmental benefits of substituting traditional Portland concrete with AAC.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
Great complexity characterizes Additive Manufacturing (AM) of metallic components via laser powder bed fusion (PBF-LB/M). Due to this, defects in the printed components (like cracks and pores) are still common. Monitoring methods are commercially used, but the relationship between process data and defect formation is not well understood yet. Furthermore, defects and deformations might develop with a temporal delay to the laser energy input. The component’s actual quality is consequently only determinable after the finished process.
To overcome this drawback, thermographic in-situ testing is introduced. The defocused process laser is utilized for nondestructive testing performed layer by layer throughout the build process. The results of the defect detection via infrared cameras are shown for a research PBF-LB/M machine.
This creates the basis for a shift from in-situ monitoring towards in-situ testing during the AM process. Defects are detected immediately inside the process chamber, and the actual component quality is determined.
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.
Weather-dependent passive thermography and thermal simulation of in-service wind turbine blades
(2023)
. To cope with the increase in the manufacturing and operation of wind turbines, wind farm operators need inspection tools that are able to provide reliable information while keeping the downtime low. Current inspection techniques require to stop the wind turbine. This work presents the current progress in the project EvalTherm, in which passive thermography is evaluated as a possible non-destructive inspection tool for operational wind turbine blades (WTBs). A methodology to obtain thermal images of rotating WTBs has been established in this project. However, the quality of the results is heavily dependent on various aspects such as weather conditions, information on the inspected WTB, damage history, etc. In this work, a section of a used WTB is simulated using finite-element modelling (FEM) as well as experimentally tested for evaluating the accuracy of the model. Such a model will provide insight into the potential thermal response of a certain structure (with specific material properties) in given weather (boundary) conditions. The model is able to provide satisfactory predictions of the thermal response of the structure, as well as indicate what thermal contrast(s) result from artificial defects introduced in the structure.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2-relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.
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.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s).
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.
Self-healing agents have the potential to restore mechanical properties and extend service life of composite materials. Thermoplastic healing agents have been extensively investigated for this purpose in epoxy matrix composites due to their strong adhesion to epoxy and their ability to fill in microcracks. One of the most
investigated thermoplastic additives for this purpose is poly(ethylene-co-methacrylic acid) (EMAA). Despite the ability of thermoplastic healing agents to restore mechanical properties, it is important to assess how the addition of thermoplastic healing agents affect properties of the original epoxy material. In this work, EMAA was added to epoxy resin and the effect of the additive on fracture toughness of epoxy was evaluated. Results indicate that although added in low concentrations, EMAA can affect fracture toughness.
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.
Recently, the photothermal determination of the thermophysical properties of concrete under lab conditions was reported. Their values are mainly needed to look at the energy consumption of buildings. Additionally, changes in their values in relation to the initial state might also be a good indicator for material quality or for moisture. The present contribution explains the photothermal method in a more general way to indicate the potential for on-site application. Secondly, a special application case is regarded: the detection of moisture in concrete. Two concrete samples were soaked with water, followed by a drying period, to obtain different levels of water penetration. The water contents were determined by weighing, and the photothermal response was measured. The results show a large influence on the measured temperature transients, which is larger than expected from the original simple model. They clearly provide two points: the photothermal method is suited to detect moisture in concrete, but the magnitude of the actual measurement effect is not yet understood.
This contribution summarizes actual developments and draft fundamental teaching topics in the field of nondestructive testing in civil engineering (NDT-CE). It is based on the first memorandum on teaching and research in the field of NDT-CE at German speaking universities and provides an overview of the academic education and highlights possible focuses, especially in teaching but also takes into account noteworthy developments and topics in research in the field of NDT-CE.
Suggestions are given for the development and advancement of the teaching curricula in regards to a comprehensive and sound professional education of students in civil engineering and adjacent disciplines.
Im Europäischen EMPIR-Projekt „NanoXSpot“ (Measurement of the focal spot size of Xray tubes with spot sizes down to 100 nm) werden neue Messmethoden für Brennflecke von Röntgenröhren entwickelt. Teil des Projektes ist die Entwicklung eines zur Lochkameramethode äquivalenten Messverfahrens für kleine Brennflecke. ASTM E 1165-20, Annex A, beschreibt die Bestimmung von Brennfleckgrößen aus Kantenprofilen von Lochaufnahmen für Röntgenröhren mit Brennflecken > 50 μm. Es wurde bereits vorgeschlagen über die Analyse der richtungsabhängigen Kantenunschärfe einer Lochblende und anschließender CT-Rekonstruktion die Intensitätsverteilung des Brennflecks äquivalent zur Lochkameraaufnahme zu berechnen. Die Lochkameramethode, wie in EN 12543-2 und ASTM E 1165-20 beschrieben, ist im unteren Größenbereich für Mikrofokusröhren nicht geeignet, da Pinholes < 10 μm schwer zu fertigen sind und lange Belichtungszeiten erwartet werden. Mit Hilfe exakt gefertigter Lochblenden sowie strukturierter Targets mit
konvergierenden Strukturen wird der Bereich mit der Single-Shot-CT-Methode auf die Messung von Mikrofokusröhren, alternativ zur Vermessung von Kanten oder Strichgruppenkörpern, erweitert. Die rekonstruierten Brennfleckformen werden mit Kantenund Lochkameraaufnahmen quantitativ verglichen, um die Messgenauigkeit zu bewerten.
Außerdem werden CNR und Messzeit bestimmt, um die Wirtschaftlichkeit der Verfahren zu bewerten.
Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks.
Im Rahmen des EMPIR-Projektes NanoXSpot (Nanometre X-Ray Focal Spot Measurement) werden rückführbare Messverfahren zur Bestimmung der Brennfleckgröße und -form von Röntgenröhren mit Submikrometerauflösung (100 nm – 20 μm) entwickelt, die die Grundlage für die Erarbeitung eines Standardentwurfes bilden. Der Standardentwurf wird bei CEN TC 138 (Non-destructive testing) WG 1 (Radiographic testing) eingereicht und mit ISO und ASTM harmonisiert. Zur Untersuchung der entwickelten Methoden (Brennfleck-CT, Fit-Prozedur für Strichgruppenkörper) und des im Projekt NanoXSpot neu entworfenen Testkörpers, NxS, wurde ein Ringversuch durchgeführt. Dabei wurde sowohl der angestrebte Überlappungsbereich mit EN 12543 Teil 5 (Messung der effektiven Brennfleckgröße von Mini- und Mikrofokus-Röntgenröhren) und ASTM E 2903 (Standard Test Method for Measurement of the Effective Focal Spot Size of Mini and Micro Focus X-ray Tubes) für Brennflecke >5 μm als auch Brennflecke <5 μm untersucht. Im Mittelpunkt standen dabei Stabilität und Wiederholbarkeit der entwickelten Methoden zur Bestimmung der Brennfleckgröße. In diesem Beitrag werden die Ergebnisse des durchgeführten Ringversuches vorgestellt und diskutiert, um Schlussfolgerungen und Empfehlungen für die Vorbereitung eines erweiterten Ringversuchs mit Partnern und Stakeholdern abzuleiten.
Im Rahmen des EMPIR-Projektes NanoXSpot (Nanometre X-ray Focal Spot Measurement) werden rückführbare Messverfahren zur Bestimmung der Brennfleckgröße und -form von Röntgenröhren im Bereich von 100 nm bis 10 mm entwickelt, welche die Grundlage für die Erarbeitung mehrerer Normentwürfe bilden. Diese werden 2022 parallel bei ISO TC 135 (NDT) SC 5 (RT) und CEN TC 138 (NDT) WG 1 (RT) eingereicht. Entsprechende Normen werden auch bei ASTM vorgestellt. Die Norm EN 12543-4 (Kantenmethode) wird umgeschrieben für die Messung von Brennflecken von 1 μm – 10 mm durch Auswertung von Loch- oder Scheibenaufnahmen. Der im Rahmen des NanoXSpot-Projektes neu entwickelte NxS-Testkörper ist geeignet für den Bereich von 1 μm – 100 μm. Von 100 μm bis 10 mm können ASTM-Lochtestkörper verwendet werden, die bereits bei ASTM E 1165 beschrieben sind. Neu ist die Anwendung eines CT-Algorithmus zur Rekonstruktion der Brennflecke, um Lochblenden-basierte Brennfleckkameras, insbesondere im Bereich unter 100 μm, zu ersetzen. Ein neuer Normteil, EN 12543-6, wurde entworfen, um die Brennfleckmessung im Bereich 100 nm – 20 μm mit hoher Genauigkeit zu ermöglichen.
Verschiedene Strichgruppen-Testkörper kommen hier zum Einsatz. Das sind die JIMAPrüfkörper, ein neuer Hochkontrast-Strichgruppentestkörper von YXLON (HiCo) und der neu entwickelte Testkörper NxS. Diese werden vorgestellt. Die Messprozedur für Röhrenhersteller wird auf einer numerischen Fit-Prozedur basieren, die über einen Ringversuch im Projekt validiert wurde. Beide Normen benötigen numerische Auswertealgorithmen zur korrekten Anwendung, die als kommerzielle Software und Public Domain – Software verfügbar gemacht werden. Es wird über die zu erwartenden Standardprozeduren und die erforderliche Hardware zur Anwendung der Normen informiert.
Direct Laser Writing (DLW) and X-ray computed tomography (XCT) both offer unique possibilities in their respective fields. DLW produces full three-dimensional (3D) polymer structures on the microscale with resolutions below 100 nm. The fabricated structures can be analysed by XCT or X-ray microscopy (XRM), which incorporates additional X-ray lenses, in three dimensions down to a minimal basic spatial resolution of about 500 nm or 50 nm, respectively. In this work, two different DLW structures are analysed via XCT. Internal defects are detected and analysed for the purpose of quality control. Defects and structures with sizes down to 1.5 µm are successfully analysed. A 3D reconstruction and internal, hidden features of the fabricated structures are shown and discussed. In a first-of-its-kind study, we demonstrate the detectability of a single-voxel line inside a fabricated structure that would not be detectable with SEM or light microscopy. Furthermore, the direct fabrication on a PET substrate is shown to overcome the high X-ray absorbance of commonly used glass substrates. Attenuation spectra of SZ2080 and glass substrates are compared to a fabrication route direct on a 170 µm PET foil. The practical aspects of XCT measurements for DLW structures on different substrates will be discussed.
In-situ monitoring of the Laser Powder Bed Fusion build process via bi- chromatic optical tomography
(2022)
As metal additive manufacturing (AM) is entering industrial serial production of safety relevant components, the need for reliable process qualification is growing continuously. Especially in strictly regulated industries, such as aviation, the use of AM is strongly dependent on ensuring consistent quality of components. Because of its numerous influencing factors, up to now, the metal AM process is not fully controllable. Today, expensive part qualification processes for each single component are common in industry.
This contribution focusses on bi-chromatic optical tomography as a new approach for AM in-situ quality control. In contrast to classical optical tomography, the emitted process radiation is monitored simultaneously with two temperature calibrated cameras at two separate wavelength bands. This approach allows one to estimate the local maximum temperatures during the manufacturing process, thus increases the comparability of monitoring data of different processes. A new process information level at low investment cost is reachable, compared to, e.g., infrared thermography.
In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
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.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
Für die aktive Thermografie als zerstörungsfreie Prüfmethode galt lange Zeit die Faustformel, dass die Auflösung interner Defekte/Inhomogenitäten auf ein Verhältnis von Defekttiefe/Defektgröße ≤ 1 beschränkt ist. Die Ursache hierfür liegt in der diffusiven Natur der Wärmeleitung in Festkörpern. Sogenannte Super-Resolution-Ansätze erlauben seit Kurzem die Überwindung dieser physikalischen Grenze um ein Vielfaches. Damit ergibt sich die attraktive Möglichkeit die Thermografie von einem rein oberflächensensitiven Prüfverfahren hin zu einem Verfahren mit verbesserter Tiefenreichweite zu entwickeln. Wie weit diese Entwicklung getrieben werden kann, ist Gegenstand aktueller Forschung.
Wir konnten bereits zeigen, dass diese klassische Grenze für 1D- und 2D Defektgeometrien mit Hilfe des Abscannens des Prüfkörpers mittels einzelner Laserspots und der anschließenden Anwendung von photothermischer Super-Resolution-Rekonstruktion überwunden werden kann. Bei dieser Methode wird eine Kombination aus sequenzieller räumlich strukturierter Beleuchtung und numerischen Optimierungsmethoden eingesetzt. Dies geschieht allerdings auf Kosten der experimentellen Komplexität, die zu einer langen Messdauer, großen Datensätzen und langwieriger numerischer Auswertung führt.
In dieser Arbeit berichten wir über einen neuen experimentellen Ansatz, bei dem räumlich strukturierte 2D-Beleuchtungsmuster in Verbindung mit Compressed-Sensing und Computational-Imaging-Methoden verwendet werden, um die experimentelle Komplexität deutlich zu verringern und die Methode für die Untersuchung größerer Prüfflächen nutzbar zu machen.
Der experimentelle Ansatz basiert dabei auf der wiederholten (blinden) photothermischen Anregung mit räumlich strukturierten 2D-Mustern unter Verwendung moderner Projektortechnik und eines Hochleistungslasers. In der anschließenden numerischen Rekonstruktion werden mehrere Messungen unter Ausnutzung der Joint-Sparsity der Defekte innerhalb des Prüfkörpers mittels nichtlinearer konvexer Optimierungsmethoden kombiniert. Als Ergebnis kann eine 2D-sparse Defekt-/Inhomogenitätskarte erstellt werden.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
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.
Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a μCT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods.
The knowledge of the thermal parameters of a particular concrete is essential for thermal design of a building, but also could help to identify and assess the state of a concrete structure. Active thermography has the potential to be applied onsite and to provide a fast investigation of thermal properties. In this work, three different concrete samples were investigated by active thermography in reflection and in transmission setup. It was found that this method yields the same results without direct contact as the Transient Plane Source (TPS) method as an established inspection tool.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
Sehr geehrte Damen und Herren,
liebe Leserinnen und Leser,
bereits im ersten Band der N.i.Ke. Schriftenreihe des Netzwerks zur interdisziplinären Kulturerhaltung hatte ich angeführt, dass bei der Untersuchung von kulturellen Artefakten naturwissenschaftliche Untersuchungsmethoden zunehmend an Bedeutung gewinnen, sie eigentlich unverzichtbar geworden sind.
Ein besonders einprägsames Beispiel dieser fächerübergreifenden Zusammenarbeit zwischen Geisteswissenschaften, Naturwissenschaften und Restaurierungswissenschaften war die Ausstellung Johannes Vermeer. Vom Innehalten, die in diesem Jahr in der Gemäldegalerie Alter Meister in den staatlichen Kunstsammlungen Dresden gezeigt wurde. Eigens für diese Ausstellung wurde das Gemälde Brieflesendes Mädchen am offenen Fenster einer grundlegenden Restaurierung unterzogen. Diese Restaurierung diente jedoch nicht nur der Reparatur des Bildes. Um sich dem ursprünglichen Zustand des Gemäldes zwischen 1657 und 1659 anzunähern, wurden später hinzugefügte Malschichten großflächig abgenommen. Möglich wurde dies durch unterschiedliche, bildgebende materialwissenschaftliche Untersuchungen im Vorfeld, welche die wissenschaftlichen Grundlagen für die Diskussion über den weiteren Umgang mit der Lesenden schufen.
Materialanalytische Verfahren wurden und werden ständig weiterentwickelt und optimiert. Sei es, um aus einer Vielzahl von Einzelmessungen ein Gesamtbild zu erstellen, oder die Größe einer Probenentnahme so zu reduzieren, dass von einer „minimal-invasiven“ oder „quasi nicht-invasiven“ Untersuchung ausgegangen werden kann. Nun feiert die Bundesanstalt für Materialforschung und -prüfung, zusammen mit ihren Vorgängerinstitutionen, den königlichen Versuchsanstalten, in diesem Jahr ihren einhundertfünfzigsten Geburtstag. Sie blickt damit auf eine lange Tradition materialwissenschaftlicher Analysen zurück, an deren Beginn beispielsweise die metallographischen Untersuchungen von Adolf Martens stehen. Derartige Untersuchungen bilden auch heute noch einen wesentlichen Bestandteil archäometallurgischer Forschung. Die Historie bzw. die Weiterentwicklung der Methodik lässt sich hier besonders gut nachzeichnen. Jedoch blicken auch Verfahren zur Charakterisierung weiterer Werkstoffe, wie Glas, Keramik, oder organischer Materialien auf eine lange Geschichte zurück.
Die in diesem Band der N.i.Ke.-Schriftenreihe präsentierten Beiträge dokumentieren einerseits aktuelle Forschungsarbeiten, die erst in diesem Jahr in entsprechenden Fachjournalen publiziert wurden, andererseits liegen einige der Arbeiten bereits 50 Jahre zurück. Sie bekunden damit nicht nur die materialtechnologische und historische Bandbreite der Projekte, sondern offenbaren die Vielfältigkeit kulturhistorischer oder restauratorischer Fragestellungen, zu deren Beantwortung natur- und materialwissenschaftliche Forschung wesentliche Beiträge liefern können.
Ich bedanke mich bei allen Kolleginnen und Kollegen, die zur Erstellung dieser dritten Ausgabe der N.i.Ke. Schriftenreihe des Netzwerks zur interdisziplinären Kulturerhaltung beigetragen haben. Ihre Aufsätze ermöglichen den Blick in die Vielfältigkeit materialwissenschaftlicher Untersuchungen im Bereich Kunst- und Kulturgut. Ich wünsche Ihnen viel Spaß auf dieser kleinen Zeitreise.
The triaxial distribution of the residual stress in laser powder bed fused austenitic steel 316L was determined by X-ray and neutron diffraction. The residual stress analysis results were linked to the thermal history of the specimens, which were manufactured with varying inter-layer-times and scanning velocities. A clear link between the in-process temperature of the specimens and the residual stress was found, based on in-situ monitoring data.
Industrial processes release substantial quantities of waste heat, which can be harvested to generate electricity. At present, the conversion of low grade waste heat to electricity relies solely on thermoelectric materials, but such materials are expensive and have low thermodynamic efficiencies. Although thermomagnetic materials may offer a promising alternative, their performance remains to be evaluated, thereby hindering their real-world application. Here, the efficiency and cost effectiveness of thermomagnetic materials are evaluated for the usage in motors, oscillators, and generators for converting waste heat to electricity. The analysis reveals that up to temperature differences of several 10 K, the best thermomagnetic materials have the potential to compete with thermoelectric materials. Importantly, it is found that the price per watt of some thermomagnetic materials is much lower compared to that of present-day thermoelectrics, which can become competitive with conventional power plants. This materials library enables the selection of the best available thermomagnetic materials for harvesting waste heat and gives guidelines for their future development.
Detection of surface breaking cracks using flying line laser thermography: A Canny-based algorithm
(2021)
In this work, we introduce a new algorithm for effectual crack detection using flying line laser thermography, based on the well-known Canny approach. The algorithm transforms the input thermographic sequence into an edge map. Experimental measurements are performed on a metallic component that contains surface breaking cracks due to industrial use. The specimen is tested using flying line thermography at different scanning speeds and laser input powers. Results obtained with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography. The proposed Canny-based algorithm can be used in automated systems for thermographic non-destructive testing.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Seit 1999 existiert das MIL-HDBK 1823, die us-amerikanische Leitfaden zur Erstellung einer Probability of Detection (POD; Fehlerdetektionswahrscheinlichkeit). Obwohl das Dokument als internationaler Stand der Technik angesehen wird, ist die allgemeine Anwendung in der ZfP-Praxis von einigen Nachteilen begleitet:
1. Die beschriebenen POD‘s bilden ausschließlich die Grundlagen-POD ab, jeweils mit nur einem Fehlerparameter und einem Signalparameter. Modernere Ansätze werden hier nicht adressiert.
2. Die aktuelle Version verzichtet fast vollständig auf konkrete mathematische Formulierungen.
3. Die Richtlinie richtet sich hauptsächlich an die militärische Luftfahrt.
4. Das Dokument ist nur in englischer Sprache verfügbar.
Mit Hilfe eines BMWi-Förderprojekts wird ein weiterführendes Regelwerk auf Basis der MIL-HDBK 1823 unter deutscher Federführung erarbeitet, welches die Anwendung der POD-Berechnung auf eine breitere Basis stellt und die oben genannten Nachteile der MIL-HDBK 1823 behebt.
Das vorgestellte Projekt „normPOD“ wird eine normative, branchenübergreifend einsetzbare Beschreibung des Vorgehens bei der Berechnung einer POD zur Zuverlässigkeitsbewertung zerstörungsfreier Prüfungen entwickeln. Hierbei wird Klarheit über unterschiedliche POD-Ansätze geschaffen und anhand praktischer Beispiele aus Maschinenbau und Bauwesen ein Leitfaden für den Praxiseinsatz erarbeitet.
Das Poster gibt einen Überblick über die geplanten Arbeiten innerhalb des Projekts und lädt die Beteiligten der DGZfP-Jahrestagung zur Diskussion ein, um eine breite Basis und Akzeptanz für die entwickelten Berechnungsverfahren zu schaffen, insbesondere in Hinblick auf ZfP 4.0 und Digitalisierung.
To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering
(2021)
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn.
Undetected and undesired microstructural variations in components produced by laser powder bed fusion are a major challenge, especially for safety-critical components. In this study, an in-depth analysis of the microstructural features of 316L specimens produced by laser powder bed fusion at different levels of volumetric energy density and different levels of inter layer time is reported. The study has been conducted on specimens with an application relevant build height
(>100 mm). Furthermore, the evolution of the intrinsic preheating temperature during the build-up of specimens was monitored using a thermographic in-situ monitoring set-up. By applying recently determined emissivity values of 316L powder layers, real temperatures could be quantified. Heat accumulation led to preheating temperatures of up to about 600 °C. Significant differences in the preheating temperatures were discussed with respect to the individual process parameter combinations, including the build height. A strong effect of the inter layer time on the heat accumulation was observed. A shorter inter layer time resulted in an increase of the preheating temperature by more than a factor of 2 in the upper part of the specimens compared to longer inter layer times. This, in turn, resulted in heterogeneity of the microstructure and differences in material properties within individual specimens. The resulting differences in the microstructure were analyzed using electron back scatter diffraction and scanning electron microscopy. Results from chemical analysis as well as electron back scatter diffraction measurements indicated stable conditions in terms of chemical alloy composition and austenite phase content for the used set of parameter combinations. However, an increase of the average grain size by more than a factor of 2.5 could be revealed within individual specimens. Additionally, differences in feature size of the solidification cellular substructure were examined and a trend of increasing cell sizes was observed. This trend was attributed to differences in solidification rate and thermal gradients induced by differences in scanning velocity and preheating temperature. A change of the thermal history due to intrinsic preheating could be identified as the main cause of this heterogeneity. It was induced by critical combinations of the energy input and differences in heat transfer conditions by variations of the inter layer time. The microstructural variations were directly correlated to differences in hardness.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
The photoacoustic measurement technique is a powerful yet underrepresented method to characterize the thermal transport properties of thin films. For the case of isotropic low thermal diffusivity samples, such as glasses or polymers, we demonstrate a general approach to extract the thermal conductivity with a high degree of significance. We discuss in particular the influence of thermal effusivity, thermal diffusivity, and sample layer thickness on the significance and accuracy of this measurement technique. These fundamental thermal properties guide sample and substrate selection to allow for a feasible thermal transport characterization. Furthermore, our data evaluation allows us to directly extract the thermal conductivity from this transient technique, without separate determination of the volumetric heat capacity, when appropriate boundary conditions are fulfilled.
Using silica, poly(methyl methacrylate) (PMMA) thin films, and various substrates (quartz, steel, and silicon), we verify the quantitative correctness of our analytical approach.