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Organisationseinheit der BAM
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Paper des Monats
- ja (4)
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.
Layer-by-layer additive manufacturing (AM) by means of laser-powder bed Fusion (L-PBF) offers many prospects regarding the design of lattice structures used, for example, in gas turbines. However, defects such as bulk porosity, Surface roughness, and re-entrant features are exacerbated in nonvertical structures, such as tilted struts. The characterization and quantification of these kinds of
defects are essential for the correct estimation of fracture and fatigue properties.
Herein, cylindrical struts fabricated by L-PBF are investigated by means of X-ray computed tomography (XCT), with the aim of casting light on the dependence of the three kinds of defects (bulk porosity, surface roughness, and re-entrant features) on the build angle. Innovative analysis methods are proposed to correlate shape and position of pores, to determine the angular-resolved Surface roughness, and to quantify the amount of re-entrant surface features, q. A meshing of the XCT surface enables the correlation of q with the classical Surface roughness Pa. This analysis leads to the conclusion that there is a linear correlation between q and Pa. However, it is conjectured that there must be a threshold of surface roughness, below which no re-entrant features can be build.
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.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible.
Fiber-reinforced concretes (FRCs) offer significant improvements in tensile strength and durability compared to most other concrete mixes.
However, for safe and efficient use of FRC in large structures, anisotropy of fiber orientation needs to be understood and properly controlled. In this project, both cored samples extracted from a FRC slab and FRC samples cast individually in molds were assessed using X-ray computed tomography (CT) and measurements of fiber orientation were extracted from the resulting CT images. These results showed that fibers within the slab were highly anisotropic in orientation while fibers in individually cast samples showed a much more heterogeneous distribution of orientations.
This indicates that fiber orientation is highly dependent on the casting process and suggests that FRC can only be safely and efficiently utilized if anisotropic fiber orientation is properly accounted for during design and optimized casting methods are used during construction.
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.
The hard X-ray beamline BAMline at BESSY II (Berlin, Germany) has now been in service for 20 years. Several improvements have been implemented in this time, and this review provides an overview of the imaging methods available at the BAMline. Besides classic full-field synchrotron X-ray computed tomography (SXCT), also absorption edge CT, synchrotron X-ray refraction radiography (SXRR), and synchrotron X-ray refraction tomography (SXRCT) are used for imaging. Moreover, virtually any of those techniques are currently coupled in situ or operando with ancillary equipment such as load rigs, furnaces, or potentiostats. Each of the available techniques is explained and both the current and the potential usage are described with corresponding examples. The potential use is manifold, the examples cover organic materials, composite materials, energy-related materials, biological samples, and materials related to additive manufacturing. The article includes published examples as well as some unpublished applications.
The Measurement- and Model-based Structural Analysis (MeMoS) integrates a finite element model into least squares adjustment and thus allows to evaluate a mechanical model and measurements in a combined analysis. To examine the capability to detect and localise damage using this integrated analysis MeMoS, a small-scale truss bridge made of aluminium profiles is built as a test specimen for this purpose.
About the Role of Interfaces on the Fatigue Crack Propagation in Laminated Metallic Composites
(2021)
The influence of gradients in hardness and elastic properties at interfaces of dissimilar materials in laminated metallic composites (LMCs) on fatigue crack propagation is investigated experimentally for three different LMC systems: Al/Al-LMCs with dissimilar yield stress and Al/Steel-LMCs as well as Al/Ti/Steel-LMCs with dissimilar yield stress and Young’s modulus, respectively. The damage tolerant fatigue behavior in Al/Al-LMCs with an alternating layer structure is enhanced significantly compared to constituent monolithic materials. The prevalent toughening mechanisms at the interfaces are identified by microscopical methods and synchrotron X-ray computed tomography. For the soft/hard transition, crack deflection mechanisms at the vicinity of the interface are observed, whereas crack bifurcation mechanisms can be seen for the hard/soft transition. The crack propagation in Al/Steel-LMCs was studied conducting in-situ scanning electron microscope (SEM) experiments in the respective low cycle fatigue (LCF) and high cycle fatigue (HCF) regimes of the laminate. The enhanced resistance against crack propagation in the LCF regime is attributed to the prevalent stress redistribution, crack deflection, and crack bridging mechanisms. The fatigue properties of different Al/Ti/Steel-LMC systems show the potential of LMCs in terms of an appropriate selection of constituents in combination with an optimized architecture. The results are also discussed under the aspect of tailored lightweight applications subjected to cyclic loading.
The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt.