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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.
Digital Coplanar Translational Laminography (CTL) is a radiographic imaging technique that permits the visualization of structural details and discontinuities in the object of interest after 3D reconstruction from multiple projections. This technique becomes quite competitive regarding to computed tomography in situations of arrangements in which no complete rotation of the X-ray source - detector system around the object of interest is accessible or in cases where the part to be inspected has a high geometric asymmetry. The literature does not show many studies about image quality for different laminographic reconstructions and scan geometries.
Different methodologies for measuring the Modulation Transfer Function (MTF) as a key image quality parameter for the aforementioned technique are applied. The MTF was measured using different approaches, presampled MTF or sampled MTF, both based on the Fourier Transform-Magnitude Spectrum or the square wave
MTF using line pattern gauges. A sample known as Siemens Star with a converging line pattern was used to measure the circular square wave MTF. This circular MTF provides information on the direction dependent contrast transfer related to the measurement conditions, the reconstruction algorithms and the translational scan direction. For this purpose, several MTF measurements were performed in multiple angular directions. Beyond it, reconstructions were performed using different algorithms (Filtered Shifted Average) and an iterative one (MART – Multiplicative Algebraic Reconstruction Technique). Image quality analysis were done for both reconstruction methods. MART showed an image quality improvement for challenging conditions for laminographic arrangements, such as for a low number of projections (down to 25 for this study, considered as limited view, limited
angle reconstruction). Additionally, a Gadolinium gauge with parallel line patterns within a range from 10 μm to 1000 μm was used to compare the square wave MTF with the sine MTFs, measured by the different techniques mentioned above. The results show that the MTF techniques presented are equivalent in this study. The results obtained for this Gadolinium plate showed a good agreement between MTF10% and SRb image measurements. The methodology presented here is used for the characterization of the frequency dependent spatial resolution of measurements obtained from different laminographic reconstruction techniques and scan geometries. Finally, from the acquisitions and reconstructions obtained with the Siemens Star, cross laminographic images were calculated in order to improve the direction dependent sensitivity. The results showed a better detectability for cross laminographic images in comparison to unidirectional coplanar translational laminographic images covering dead zones.
Pulse thermography (PT) has proven to be a valuable non-destructive testing method to identify and quantify defects in fiber-reinforced polymers. To perform a quantitative defect characterization, the heat diffusion within the material as well as the material parameters must be known. The heterogeneous material structure of glass fiber-reinforced polymers (GFRP) as well as the semitransparency of the material for optical excitation sources of PT is still challenging. For homogeneous semitransparent materials, 1D analytical models describing the temperature distribution are available.
Here, we present an analytical approach to model PT for laterally inhomogeneous semitransparent materials.We show the validity of the model by considering different configurations of the optical heating source, the IR camera, and the differently coated GFRP sample. The model considers the lateral inhomogeneity of the semitransparency by an additional absorption coefficient. It includes additional effects such as thermal losses at the samples surfaces, multilayer systems with thermal contact resistance, and a finite duration of the heating pulse. By using a sufficient complexity of the analytical model, similar values of the material parameters were found for all six investigated configurations by numerical fitting.
Since nuclear magnetic resonance with focus on 1H protons is highly sensitive to pore filling fluids, it is nowadays often applied for the investigation of porous media. Mainly in materials research and especially in the field of non-destructive testing in civil engineering it is increasingly used. Scientific questions about and based on NMR meanwhile cover a broad spectrum. To give an overview, we have reviewed various studies dealing with the determination of moisture contents and parameters such as the pore-size distribution, surface relaxivity, porosity, etc. In some papers, the monitoring of moisture transport in connection with degradation processes or admixtures was the main objective. In other papers, NMR was used for pore space analysis or even applied on site to assess the state of conservation of cultural heritage. Building materials that have been investigated in the presented studies are for example cement, concrete, woods, sandstones etc. In this paper, short descriptions and the significant results of the reviewed articles are summarized and their measurement problems and discrepancies are pointed out. A special feature of this review article is the concise tabular compilation of determined 𝑇1 and 𝑇2 relaxation times, as well as of surface relaxivity values for various materials and components. Finally, relevant aspects are summed up and conclusions about the increasing potential of NMR relaxometry for investigations of porous building materials are drawn, followed by an outlook about future applications and the need for technical development.
Low-velocity impact damages were monitored in-situ using an infrared camera before, during and after Impact load. Thermal images were recorded as a function of time at the front side (impact) as well as at the rear side of the plates using a high frame rate. In CFRP and GFRP specimens with different thicknesses and made of various types of fibers and matrix materials, different kind of damages were observed. The sizes of the heated areas being related to the damages and the amount of energy dissipated into heat was determined quantitatively as a function of impact energy and are a measure of the resistance of the different materials against impact load.
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.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Reliability of NDT is affected by human factors, which have thus far received the least amount of attention in the reliability assessments. With increased use of automation, in terms of mechanised testing (automation-assisted inspection and the corresponding evaluation of data), higher reliability standards are believed to have been achieved. However, human inspectors, and thus human factors, still play an important role throughout this process and the risks involved in this application are unknown. The aim of this study was to explore for the first time the risks associated with mechanised NDT and find ways of mitigating their effects on the inspection performance. Hence, the objectives were to identify and Analyse potential risks in mechanised NDT and devise measures against them. To address those objectives, a risk assessment in form of a Failure Modes and Effects Analysis (FMEA) was conducted. This analysis revealed potential for failure during both the acquisition and evaluation of NDT data that could be assigned to human, technology, and organisation. Since the existing preventive measures were judged to be insufficient to defend the system from identified failures, new preventive measures were suggested.
The one-dimensional thermal quadrupole method is used to evaluate a pulsed thermography measurement at delaminations in a glass-fiber reinforced plastic plate quantitatively. The large-scale delaminations have been induced by tension overload and are air-filled and are usually located at the same depth as the notch bottom of a notch on the rear side. While classical evaluation methods like pulsed phase thermography and thermal Signal reconstruction are focused on the delamination depth only, the thermal quadrupole method determines spatially resolved two parameters for delaminations, delamination depth and local thermal resistance. Interestingly, lateral heat flows do not disturb this kind of depth evaluation.
High quality tubular products are essential to the oil and gas industry. Quality control during their production focuses on the non-destructive detection of surface defects. The structured light technique is a candidate for the challenge to detect, monitor and evaluate such defects in real-time.
In the present study the automatic processing of structured light measurements is performed and validated. The algorithm for the automatic Analysis of inspection data has an advantage over current data evaluation methods based on individual assessments of operators.
Photothermal radiometry with an infrared camera allows the contactless temperature measurement of multiple surface pixels simultaneously. A short light pulse heats the sample. The heat propagates through the sample by diffusion and the corresponding temperature evolution is measured at the sample’s surface by an infrared camera. The main drawback in radiometric imaging is the loss of the spatial resolution with increasing depth due to heat diffusion, which results in blurred images for deeper lying structures. We circumvent this information loss due to the diffusion process by using blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The structured illumination is realized by parallel laser lines from a vertical-cavity surface-emitting laser (VCSEL) array controlled by a random binary pattern generator. By using 150 different patterns of structured illumination and our iterative joint sparsity algorithm, it was possible to resolve 1 mm thick lines at a distance down to 0.5 mm, which results in a resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenous illuminated thermographic reconstruction.