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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 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.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
A concept for the application of a magnetocaloric device in energy-efficient air conditioners is introduced. In order to evaluate this concept, a test stand has been developed equipped with a magnetic field source providing about a 1.5-T flux density change into which different Regenerator geometries can be implemented and evaluated. A processing route for the production of profiled magnetocaloric LaFeSiMn-based composite plates by tape casting is presented. The processed plates show a maximum isothermal entropy change of about 3.8 J kg−1 K−1 at a magnetic field change of 1.5 T at 285 K. The hydraulic and thermal performance of regenerator geometries that can be realized by profiled plates is assessed by calculations.
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.
Pulse and flash thermography are experimental techniques which are widely used in the field of non-destructive testing for materials characterization and defect detection. We recently showed that it is possible to determine quantitatively the thickness of semitransparent polymeric solids by fitting of results of an analytical model to experimental flash thermography data, for both transmission and reflection configuration. However, depending on the chosen experimental configuration, different effective optical absorption coefficients had to be used in the model to properly fit the respective experimental data, although the material was always the same. Here, we show that this effect can be explained by the wavelength dependency of the absorption coefficient of the sample material if a polychromatic light source, such as a flash lamp, is used. We present an extension of the analytical model to describe the decay of the heating irradiance by two instead of only one effective absorption coefficient, greatly extending its applicability. We show that using this extended model, the experimental results from both measurement configurations and for different sample thicknesses can be fitted by a single set of parameters. Additionally, the deviations between experimental and modeled surface temperatures are reduced compared to a single optimized effective absorption coefficient.
Additively manufactured test specimens made of polyamide 12 (PA 12) by Laser Sintering as well as of acrylonitrile butadiene styrene (ABS) by Fused Layer Modelling, were characterised with active thermography directly after manufacturing and after artificial weathering. For this, two different excitation methods (flash and pulse heating) were used and compared, regarding their suitability for the detection of constructed and imprinted defects inside the test specimens. To increase the quality of the thermograms, data processing methods like thermal signal reconstruction (TSR) and Fourier Transformation after TSR were applied. To further investigate the long-term stability of the additively manufactured test specimens towards environmental stress, like UV radiation, heat, humidity, water contact and frost with active thermography, an artificial weathering test over 2000 hours (~3 months) was applied to the specimens. The monitoring of the changes in the optical properties of the weathered plastics was supplemented by spectral reflectance and UV/VIS spectroscopy.
In den letzten zwei Jahren wurden 11 neue Standards/Standardrevisionen zur RT veröffentlicht (ohne Strahlenschutz) und 5 werden dafür gestrichen. Es wird über die neuen Anforderungen ausgewählter Normen bzw. Normrevisionen informiert und insbesondere auch über die veränderten Parameter, die bei Prüfpraxis und bei der Klassifizierung zu berücksichtigen sind. Das wichtigste Projekt ist die Revision der DIN EN ISO 17636-1, -2, RT von Schweißverbindungen, in ISO TC 44 SC 5 WG 1. Die Standards zur Schweißnahtprüfung in der Kerntechnik, DIN 25435, wurden ins Englische übertragen und bei ISO TC 85 SC 6 bearbeitet. Hauptaktivität bei ASTM ist gegenwärtig die Überarbeitung der CT-Standards und die Berücksichtigung der Anforderungen an das dimensionelle Messen. Der Guide ASTM E 1441 zur Bestimmung von MTF, Kontrast-Detail-Funktion (CDF) und Kontrast-Diskrimination-Diagramm (CDD) wird revidiert. E 2445 zur CR Long Term Stability wird ebenfalls revidiert. Die Revision soll auch in die ISO 16371-1 eingehen. Dazu ist ein Round Robin-Test geplant. Auch die Standards zur Durchstrahlungsprüfung auf Korrosion und Ablagerungen in Rohren mit Röntgen- und Gammastrahlen (EN 16407-1, -2, 2014) wurden revidiert. Diese Revision wurde als DIN EN ISO 20769-1, -2 2018 veröffentlicht und EN 16407 wird zurückgezogen. Die Revision der Standards EN 12543 und EN 12679 zur Messung der Brennfleckgröße und der Strahlergröße ist mit Verzögerung in Überarbeitung, um die digitalen Detektoren und Messmöglichkeiten mit Bildverarbeitungsprogrammen zu berücksichtigen. Ein Entwurf zur Messung der Brennfleckgröße von nano-Fokusröhren wird derzeit vom Europäischen Metrologie-Programm EMPIR gefördert. Zur radiographischen Gussteilprüfung wurde der Standard EN 12681 revidiert und 2018 veröffentlicht. Er wurde um Zulässigkeitsgrenzen erweitert und die digitale Radiographie wurde in Teil 2 berücksichtigt.
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.