5 Werkstofftechnik
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Organisationseinheit der BAM
- 5 Werkstofftechnik (70) (entfernen)
Additive manufacturing (AM) offers significantly greater freedom of design compared to conventional manufacturing processes since the final parts are built layer by layer. This enables metal AM, also known as metal 3D printing, to be utilized for improving efficiency and functionality, for the production of parts with very complex geometries, and rapid prototyping. However, despite many technological advancements made in recent years, several challenges hinder the mass adoption of metal AM. One of these challenges is mechanical anisotropy which describes the dependency of material properties on the material orientation. Therefore, in this work, stainless steel 316L parts produced by laser-based powder bed fusion are used to isolate and understand the root cause of anisotropy in AM parts. Furthermore, an efficient and accurate multiscale numerical framework is presented for predicting the deformation behavior of actual AM parts on the macroscale undergoing large plastic deformations. Finally, a novel constitutive model for the plastic spin is formulated to capture the influence of the microstructure evolution on the material behavior on the macroscale.
Age-hardenable aluminum alloys undergo precise heat treatments to yield nanometer-sized precipitates that increase their strength and durability by hindering the dislocation mobility. Tensile tests provide mechanical properties, while microstructure evaluation relies on transmission electron microscopy (TEM), specifically the use of dark-field TEM images for precise dimensional analysis of the precipitates. However, this manual process is time consuming, skill dependent, and prone to errors and reproducibility issues.
Our primary goal is to digitally represent these processes while adhering to FAIR principles. Ontologies play a critical role in facilitating semantic annotation of (meta)data and form the basis for advanced data management. Publishing raw data, digital workflows, and ontologies ensures reproducibility.
This work introduces innovative solutions to traditional bottlenecks and offers new perspectives on digitalization challenges in materials science. We support advanced data management by leveraging knowledge graphs and foster collaborative and open data ecosystems that potentially revolutionize materials research and discovery.
Advanced light weight applications like aircrafts and wind turbine blades are made of fibre reinforced plastics (FRP) with continuous fibre reinforcement and must withstand a high thermo-mechanical cyclic loading. The quality of the fibre matrix interface has a high impact on the fatigue life and was continuously improved over the years since the 50th. The fatigue life of glass fibre reinforced plastics (GFRP) used in aircraft industry is 10 to 100 times higher compared to glass fibre non crimp fabrics used for wind turbine blades.
To assure a constant and reliable high quality and strength of reinforcement fibres, synthetic fibre production is state of the art (CF, GF). There is a need for recycling GFRP and CFRP waste due to the upcoming use. Pyrolysis and solvolysis are more expensive than the mechanical route however enable a more sustainable recycling. Natural fibres and recycled synthetic fibres have a high scatter in quality and strength. Hence it is a challenge to optimize the production / recycling processes to get a reliable quality for any demanding (second life) application.
Chemical routes for using renewables resources and recycling, is going to be a good approach especially for polymer-matrix systems to get 100% quality (back) compared to the state of the art.
Finally, a proper design, life-time extension and repair is preferable to recycling to keep the carbon footprint as low as possible.
Im Rahmen des 2. BAM-Akademie Info-Tages "Nano or not Nano" wurde die OECD TG 124 "Volume Specific Surface Area of Manufactured Nanomaterials" vorgestellt. Der Vortrag beschreibt detailliert das Messverfahren der He-Gaspyknometrie zur Bestimmung der Skelettdichte von Pulvern und geht auf Anwendbarkeit, Besonderheiten bei Nanopulvern und wichtige Einstellparameter für die Messung ein.
Im Rahmen des 2. BAM-Akademie Info-Tages "Nano or not Nano" wurde die OECD TG 124 "Volume Specific Surface Area of Manufactured Nanomaterials" vorgestellt.
Der Vortrag beschreibt die Bestimmung der spezifischen Oberfläche von dispersen und/oder porösen Pulvern mittels Gasadsorption nach dem BET-Verfahren.
Es wird auf die Anwendbarkeit der Methode eingegangen und es werden praktische Hinweise zur Probenvorbereitung und Messung von Nanomaterialien gegeben.
Brinell test ontology (BTO)
(2024)
Brinell Test Ontology (BTO) has developed for representing the Brinell testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 6506-1 standard.
Versions info:
V2 developed using BFO+CCO top-level ontologies.
V3 developed using EMMO+CHAMEO top-level ontologies.
V4 developed using PROVO+PMDco top-level ontologies.
V5 developed using BFO+IOF top-level ontologies.
Repositories:
GitLab: https://gitlab.com/kupferdigital/process-graphs/brinell-hardness-test
GitHub: https://github.com/HosseinBeygiNasrabadi/Brinell-Test-Ontology-BTO-
MatPortal: https://matportal.org/ontologies/BTO
IndustryPortal: https://industryportal.enit.fr/ontologies/BTO
Powder bed technologies are amongst the most successful Additive Manufacturing (AM) techniques. The application of these techniques to most ceramics has been difficult so far, because of the challenges related to the deposition of homogeneous powder layers when using fine powders.
In this context, the "layerwise slurry deposition" (LSD) has been developed as a layer deposition method enabling the use of powder bed AM technologies also for advanced ceramic materials. The layerwise slurry deposition consists of the layer-by-layer deposition of a ceramic slurry by means of a doctor blade, in which the slurry is deposited and dried to achieve a highly packed powder. Not only very fine, submicron powders can be processed with low organics, but also the dense powder bed provides excellent support to the parts built. The LSD technology can be combined with binder jetting to develop the so-called “LSD-print” process. LSD-print combines the high-speed printing of binder jetting with the possibility of producing a variety of high-quality ceramics with properties comparable to traditional processing.
The latest development of this technology shows that it is possible to print ceramic parts in a continuous process by depositing a layer onto a rotating platform, growing a powder bed following a spiral motion. The unique mechanical stability of the layers in LSD-print allows to grow a powder bed several centimeters thick without any lateral support. The continuous layer deposition allows to achieve a productivity more than 10X higher compared to the linear deposition, approaching a build volume of 1 liter/hour.
The current lack of quantitative knowledge on processing-microstructure–property relationships is one of the major bottlenecks in today’s rapidly expanding field of additive manufacturing. This is centrally rooted in the nature of the processing, leading to complex microstructural features. Experimentally-guided modeling can offer reliable solutions for the safe application of additively manufactured materials. In this work, we combine a set of systematic experiments and modeling to address creep anisotropy and its correlation with microstructural characteristics in laser-based powder bed fusion (PBF-LB/M) additively manufactured Inconel-738LC (IN738LC). Three sample orientations (with the tensile axis parallel, perpendicular, and 45° tilted, relative to the building direction) are crept at 850 °C, accompanied by electron backscatter secondary diffraction (EBSD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) investigations. A crystal plasticity (CP) model for Ni-base superalloys, capable of modeling different types of slip systems, is developed and combined with various polycrystalline representative volume elements (RVEs) built on the experimental measurements. Besides our experiments, we verify our modeling framework on electron beam powder bed fusion (PBF-EB/M) additively manufactured Inconel-738LC. The results of our simulations show that while the crystallographic texture alone cannot explain the observed creep anisotropy, the superlattice extrinsic stacking faults (SESF) and related microtwinning slip systems play major roles as active deformation mechanisms. We confirm this using TEM investigations, revealing evidence of SESFs in crept specimens. We also show that the elongated grain morphology can result in higher creep rates, especially in the specimens with a tilted tensile axis.
In this presentation, the results of the determination of the diffraction and single-crystal elastic constants of laser powder bed fused Inconel 718 are presented. The analysis is based on high-energy synchrotron diffraction experiments performed at the Deutsches Elektronen-Synchrotron. It is shown that the characteristic microstructure of laser powder bed fused Inconel 718 impacts the elastic anisotropy and therefore the diffraction and single-crystal elastic constants. Finally, the consequences on the diffraction-based residual stress determination of laser powder bed fused Inconel 718 are discussed.
Laser powder bed fusion (PBF-LB/M) of metallic alloys is a layer-wise additive manufacturing process that provides significant scope for more efficient designs of components, benefiting performance and weight, leading to efficiency improvements for various sectors of industry. However, to benefit from these design freedoms, knowledge of the high produced induced residual stress and mechanical property anisotropy associated with the unique microstructures is critical. X-ray and neutron diffraction are considered the benchmark for non-destructive characterization of surface and bulk internal residual stress. The latter, characterized by the high penetration power in most engineering alloys, allows for the use of a diffraction angle close to 90° enabling a near cubic sampling volume to be specified. However, the complex microstructures of columnar growth with inherent crystallographic texture typically produced during PBF-LB/M of metallics present significant challenges to the assumptions typically required for time efficient determination of residual stress. These challenges include the selection of an appropriate set of diffraction elastic constants and a representative lattice plane suitable for residual stress analysis. In this contribution, the selection of a suitable lattice plane family for residual stress analysis is explored. Furthermore, the determination of an appropriate set of diffraction and single-crystal elastic constants depending on the underlying microstructure is addressed.
In-situ loading experiments have been performed at the Swiss Spallation Neutron Source with the main scope to study the deformation behaviour of laser powder bed fused Inconel 718. Cylindrical tensile bars have been subjected to an increasing mechanical load. At pre-defined steps, neutron diffraction data has been collected. After reaching the yield limit, unloads have been performed to study the accumulation of intergranular strain among various lattice plane families.
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future.
For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples.
All infrastructural parts were developed by a consortium consisting of the Fraunhofer ISC in Würzburg, the Friedrich-Schiller-University Jena (OSIM), the Clausthal University of Technology (INW) and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) as part of a joint project of the German research initiative MaterialDigital.
This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web.
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD)
(2024)
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
Laser Powder Bed Fusion (PBF-LB/M) of AISI 316L stainless steel has gained popularity due to its exceptional capacity to produce complex geometries and hierarchical microstructures, which can increase the yield strength while maintaining good ductility. Nevertheless, owing to high thermal gradients encountered during the process, the as printed 316L stainless steel often exhibit microstructural heterogeneities and residual stresses, which can limit its performance in demanding environments. Hence, employing heat treatments which balance the reduction of residual stresses while retaining improved static strength may be beneficial in various scenarios and applications. This study investigates the impact of post-processing heat treatments on the microstructure of 316L stainless steel manufactured via PBF-LB/M, along with its correlation with micro-hardness properties. To this end, 6 different heat treatments, i.e., 450 °C for 4h, 700 °C for 1h, 700 °C for 3h, 800 °C for 1h, 800 °C for 3h, and 900 °C for 1h, were applied to different specimens and Vickers hardness measurements (HV1) were performed in all states. At 800 °C, although the cellular structure appears to be retained, there is an observable increase in cellular size. However, while treatments exceeding 900 °C indicate no significant grain growth compared to other conditions, the cellular structure is entirely dissolved, which leads to a reduced Vickers hardness. The effect of the heat treatments on other microstructural features such as grain size and morphology, melt pool boundaries (MPB), crystallographic texture, chemical segregation, dispersoids and phase stability are also discussed in the present work
Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen.
Several studies have been shown that the electron beam can be used to create nanomaterials from microparticle targets in situ in a transmission electron microscope (TEM). Here, we show how this method has to be modified in order to synthesize plasmonic gold nanoparticles (NPs) on insulating silicon oxide substrate by employing a scanning electron microscope with a comparatively low acceleration voltage of 30 kV. The synthesized NPs exhibit a random distribution around the initial microparticle target: Their average size reduces from 150 nm to 3 nm with growing distance to the initial Au microparticle target. Similarly, their average distance increases. The synthesized NP assemblies therefore show distinctly different plasmonic behaviour with growing distance to the target, which allows to study consequences of random hybridization of surface plasmon in disordered system, such as Anderson localization. To reveal the surface plasmons and their localization behaviour we apply electron energy loss spectroscopy in the TEM.
The characterization of really unknown phases typically uses 70 to 150 reflectors for lattice metric calculation. The determination of the lattice parameters follows with 4% accuracy. Including a Z correction up to 1% can be reached. The precision of the lattice parameters ratios (a:b:c) is, however, better than 0.1%.
Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web technologies (SWT) are used since they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use.
The project ‘platform MaterialDigital’ (PMD, https://materialdigital.de) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are semantically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data.
Accordingly, this presentation shows the efforts taken within the PMD project towards the digitalization in MSE such as the development of the mid-level PMD core ontology (PMDco, https://github.com/materialdigital/core-ontology). Furthermore, selected results of a PMD partner project use case addressing data and knowledge management from synthesis, production, and characterization of materials are shown.