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In vielen Umgebungen, besonders bei hohen Temperaturen, korrosiven Umgebungen oder auf bewegten oder schlecht zugänglichen Flächen, kann die Temperatur nicht oder nur mit nicht akzeptablem Aufwand mit Berührungsthermometern gemessen werden. Diese Umgebungsbedingungen sind unter anderem in der chemischen Industrie, der Lebensmittel-, Metall-, Glas-, Kunststoff- und Papierherstellung sowie bei der Lacktrocknung anzutreffen. In diesen Bereichen kommen Strahlungsthermometer zum Einsatz. Der VDI-Statusreport zeigt typische Anwendungsfelder von nicht radiometrisch kalibrierten Wärmebildkameras und von radiometrisch kalibrierten Thermografiekameras. Um verlässlich mit spezifizierten Messunsicherheiten berührungslos Temperaturen zu messen, müssen die Strahlungsthermometer und Thermografiekameras nicht nur kalibriert, sondern radiometrisch und strahlungsthermometrisch umfassend charakterisiert werden. Auch die optische Materialeigenschaft, der spektrale Emissionsgrad und die Gesamtstrahlungsbilanz (Strahlung des Messobjekts und der Umgebung) sind bei der industriellen Temperaturmessung von großer Bedeutung. In den letzten Jahrzehnten ist dazu ein umfassendes technisches Regelwerk entstanden, das wir Ihnen mit diesem VDI-Statusreport vorstellen. Manche in den Richtlinien beschriebenen Kennwerte mögen abstrakt wirken. In diesem Statusreport zeigen wir an konkreten Beispielen, was diese Kenngrößen für die berührungslose Temperaturmessung bedeuten. Beispiele von Anwendungen zeigen, wo temperaturmessende Thermografiekameras und ausschließlich bildgebende Wärmebildkameras in der Praxis eingesetzt werden. Mit einer Analyse, welche Themen und Anwendungen derzeit besonders intensiv diskutiert werden, versuchen wir Trends für zukünftige Entwicklungen herauszuarbeiten.
A joint project of partners from industry and research institutions for the research and construction of an analysis system for an automated, sensor-supported sorting of construction and demolition waste will be presented. This is intended to supplement or replace the previously practiced manual sorting, which harbors many risks and dangers for the staff and only enables obvious, visually detectable differences for separation. The method of laser-induced breakdown spectroscopy is to be used in combination with hyperspectral sensors. Due to the jointly processed information (data fusion), this should lead to a significant improvement in the separation of types. In addition to the sorting of different materials (concrete, main masonry building materials, organic components, glass, etc.), impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.) could also be detected and separated.
The subsequent recycling and sales opportunities are examined, such as the use of recycled aggregates in concrete, the recycling of building materials containing sulphate as a gypsum substitute for the cement industry or the agglomeration of synthetic lightweight aggregates for lightweight concrete or as a substrate for green roofs. At the same time, it is investigated whether soluble components (sulfates, heavy metals, etc.) can be detected by LIBS without a wet chemical analysis and what impact the recycling materials have on the environment.
The entire value chain is examined using the example of the Berlin location in order to minimize economic / technological barriers and obstacles on a cluster level and to sustainably increase the recovery and recycling rates.
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.
Das maschinelle Lernen (ML) wurde erfolgreich zur Lösung vieler Aufgaben in der zerstörungsfreien Prüfung im Bauwesen (ZfPBau) eingesetzt. Allerdings ist die Erstellung von Referenzdaten in den meisten Fällen extrem teuer und daher viel knapper als in anderen Forschungsbereichen. Auch decken die verfügbaren Daten mitunter nur ein einziges Szenario ab, so dass die Leistungsindikatoren oft nicht die tatsächliche Leistung des ML-Modells in der praktischen Anwendung widerspiegeln. Schätzungen, die die Übertragbarkeit von einem Szenario auf ein anderes quantifizieren, sind erforderlich, um dieser Herausforderung gerecht zu werden und den Weg für Anwendungen in der Praxis zu ebnen.
In diesem Beitrag stellen wir Werkzeuge zur Beschreibung der Unsicherheit von ML in neuen ZfPBau-Szenarien vor. Zu diesem Zweck haben wir einen bestehenden Trainingsdatensatz zur Klassifizierung von Korrosionsschäden der Bewehrung in Beton um eine neue Fallstudie erweitert. Die Messungen wurden an großflächigen Betonproben mit eingebauter chloridinduzierter Korrosion des Bewehrungsstahls durchgeführt. Das Experiment simulierte den gesamten Lebenszyklus von chloridinduzierten Sichtbetonbauteilen im Labor. Unser Datensatz umfasst Potenzialfeld- und Radarmessungen. Die einzigartige Fähigkeit, die Schädigung zu überwachen und eine gezielte Korrosion einzuleiten, ermöglichte es, die Daten zu labeln - was für die Konstruktion von ML-Modellen entscheidend ist. Um die Übertragbarkeit zu untersuchen, erweitern wir unser Modell um Metadaten - wie etwa Konstruktionsmerkmale des Prüfkörpers und Umweltbedingungen. Dies erlaubt es, die Veränderung dieser Merkmale in neuen Szenarien mit statistischen Methoden als Unsicherheiten auszudrücken. Wir vergleichen verschiedene auf Stichproben und statistischer Verteilung basierende Ansätze und zeigen, wie diese Methoden eingesetzt werden können, um Wissenslücken von ML-Modellen in der ZfP zu schließen
ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.
In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.
Environmentally friendly alternatives to cement are created through the synthesis of numerous base materials. The variation of their proportions alone leads to millions of materials candidates. Identifying suitable materials is very laborious; traditional systematic research in the laboratory consumes a lot of time and effort.
Sequential learning (SL) potentially speeds up the materials research process despite limited but highly complex available information. SL does not make direct predictions of material properties but ranks possible experiments according to their utility. The most promising experiments are prioritized over dead-end experiments and experiments whose outcome is already known.
Our work has shown that SL seems to be promising for cement research. So far, research has mainly focused on materials whose synthesis is faster and whose material properties require less time for development or characterization (allowing many successive experiments). Contrarily, in the case of binders, SL is only useful if few experiments lead to the desired goal, as for example, the determination of the compressive strength alone typically requires 28 days.
In research practice, experimental designs and the availability of resources often determine which data can be used - for example, when some laboratory resources are not available or deemed irrelevant to a task. As a result, new research scenarios are constantly emerging, each of which requires to demonstrate SL’s performance.
We are presenting the SLAMD app to facilitate the exploration of SL methods in numerous research scenarios. The app provides flexible and low-threshold access to AI methods via intuitive and interactive user interfaces. We deliberately pursue a software-based research approach (as opposed to code-, or script-based). On the one hand, the results are more comprehensible since we refer to a common (code) basis (’reproducible science’). On the other hand, the methods are easily accessible to all which accelerates the knowledge transfer into laboratory practice.
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.
An unusual microstructure, inherent residual stresses and void formation are the three key aspects to control when assessing metallic parts made by LPBF. This talk explains an experiment to unravel the interlinked influence of the two mechanisms for the formation of residual stresses in LPBF: the temperature gradient mechanism and constricted solidification shrinkage. The impact of each mechanism on the shape and magnitudes of the residual stress distribution is described. Combined results from neutron diffraction, X-ray diffraction, computed tomography and in-situ thermography are presented.
Also, influence of scan strategies as well as surface roughness of subjacent layers on void formation is shown. Results from computed tomography and in-situ thermography of a specimen dedicated to study the interaction of the melt pool with layers of powder underneath the currently illuminated surface are presented.
Vor allem in den letzten Jahren ist das Interesse der Industrie an der additiven Fertigung deutlich gestiegen. Die Vorteile dieser Verfahren sind zahlreich und ermöglichen eine ressourcenschonende, kundenorientierte Fertigung von Bauteilen, welche zur stetigen Entwicklung neue Anwendungsbereiche und Werkstoffe führen. Aufgrund der steigenden Anwendungsfälle, nimmt auch der Wunsch nach Betriebssicherheit unabhängig von anschließenden kostenintensiven zerstörenden und zerstörungsfreien Prüfverfahren zu. Zu diesem Zweck werden im Rahmen des von der BAM durchgeführten Themenfeldprojektes „Prozessmonitoring in Additive Manufacturing“ verschiedenste Verfahren auf ihre Tauglichkeit für den in-situ Einsatz bei der Prozessüberwachung in der additiven Fertigung untersucht. Hier werden drei dieser in-situ Verfahren, die Thermografie, die optische Emissionsspektroskopie und die Schallmissionsanalyse für den Einsatz beim Laser-Pulver-Auftragschweißen betrachtet.