5 Werkstofftechnik
Filtern
Dokumenttyp
- Vortrag (358)
- Posterpräsentation (100)
- Zeitschriftenartikel (60)
- Beitrag zu einem Tagungsband (54)
- Forschungsdatensatz (27)
- Buchkapitel (6)
- Dissertation (4)
- Beitrag zu einem Sammelband (2)
- Sonstiges (2)
- Corrigendum (1)
Sprache
- Englisch (616) (entfernen)
Referierte Publikation
- nein (616) (entfernen)
Schlagworte
- Additive Manufacturing (51)
- Corrosion (44)
- Additive manufacturing (39)
- Ontology (32)
- Microstructure (29)
- Glass (27)
- Creep (22)
- Fatigue (20)
- Transmission electron microscopy (17)
- Crack growth (16)
Organisationseinheit der BAM
- 5 Werkstofftechnik (616) (entfernen)
Determination of Distribution Function used in MCS on Safety Analysis of Hydrogen Pressure Vessel
(2019)
The test data of static burst strength and load cycle strength of composite pressure vessels are often described by GAUSSian normal or WEIBULL distribution function to perform safety analyses. The goodness of assumed distribution function plays a significant role in the inferential statistics to predict the population properties by using limited test data. Often, GAUSSian and WEIBULL probability nets are empirical methods used to validate the distribution function; Anderson-Darling and KolmogorovSmirnov tests are the mostly favorable approaches for Goodness of Fit. However, the different approaches used to determine the parameters of distribution function lead mostly to different conclusions for safety assessments. In this study, six different methods are investigated to show the variations on the rates for accepting the composite pressure vessels according to GTR No. 13 life test procedure. The six methods are: a) NormLog based method, b) Least squares regression, c) Weighted least squares regression, d) A linear approach based on good linear unbiased estimators, e) Maximum likelihood estimation and f) The method of moments estimation. In addition, various approaches of ranking function are considered. In the study, Monte Carlo simulations are conducted to generate basic populations based on the distribution functions which are determined using different methods. Then the samples are extracted randomly from a population and evaluated to obtain acceptance rate. Here, the “populations” and “samples” are corresponding to the burst strength or load cycle strength of the pressure vessels made from composite material and a plastic liner (type 4) for the storage of hydrogen. To the end, the results are discussed, and the best reliable methods are proposed.
Determination of Distribution Function used in MCS on Safety Analysis of Hydrogen Pressure Vessel
(2019)
The test data of static burst strength and load cycle strength of composite pressure vessels are often described by GAUSSian normal or WEIBULL distribution function to perform safety analyses. The goodness of assumed distribution function plays a significant role in the inferential statistics to predict the population properties by using limited test data. Often, GAUSSian and WEIBULL probability nets are empirical methods used to validate the distribution function; Anderson-Darling and Kolmogorov-Smirnov tests are the mostly favorable approaches for Goodness of Fit. However, the different approaches used to determine the parameters of distribution function lead mostly to different conclusions for safety assessments. In this study, six different methods are investigated to show the variations on the rates for accepting the composite pressure vessels according to GTR No. 13 life test procedure. The six methods are: a) Norm-Log based method, b) Least squares regression, c) Weighted least squares regression, d) A linear approach based on good linear unbiased estimators, e) Maximum likelihood estimation and f) The method of moments estimation. In addition, various approaches of ranking function are considered. In the study, Monte Carlo simulations are conducted to generate basic populations based on the distribution functions which are determined using different methods. Then the samples are extracted randomly from a population and evaluated to obtain acceptance rate. Here, the “populations” and “samples” are corresponding to the burst strength or load cycle strength of the pressure vessels made from composite material and a plastic liner (type 4) for the storage of hydrogen. To the end, the results are discussed, and the best reliable methods are proposed.
Development and characterization of starch film and the incorporation of silver nanoparticles
(2020)
Starch is one of the biopolymers being used for bioplastic synthesis. For production, starch can be combined with different plasticizers, starches from different plant sources and even with nanomaterials to improve or to add film properties. The challenge of adding these, e.g. in the form of silver nanoparticles (AgNp) is to determine the concentration so as to avoid impairing the properties of the film, agglomeration or altering the visual characteristics of the film. In this study, a starch film synthesis route and the incorporation of silver nanoparticles has been proposed in order not to alter the properties of the film while maintaining the transparency and a clear colour of the starch film. The results showed that the proposed synthesis route is promising, efficient, reproducible, fast and the film has good mechanical properties.
Development and characterization of starch film and the incorporation of silver nanoparticles
(2020)
Starch is one of the biopolymers being used for bioplastic synthesis. For production, starch can be combined with different plasticizers, starches from different plant sources and even with nanomaterials to improve or to add film properties. The challenge of adding these, e.g. in the form of silver nanoparticles (AgNp) is to determine the concentration so as to avoid impairing the properties of the film, agglomeration or altering the visual characteristics of the film. In this study, a starch film synthesis route and the incorporation of silver nanoparticles has been proposed in order not to alter the properties of the film while maintaining the transparency and a clear colour of the starch film. The results showed that the proposed synthesis route is promising, efficient, reproducible, fast and the film has good mechanical properties.
Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen.
Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation.
Efforts towards digitalization in the material science and technology community have enhanced in the last years. In 2019 the German digitalization initiative platform „MaterialDigital“1 (MD) has been started. Numerous projects concerning digitalization, including the copper related project „KupferDigital“ (copper digital) have been initiated under the umbrella of MD. The initiative strives to
address numerous issues concerning data access, exchange, security, provenance and sovereignty. Heterogeneous data origin, storage and evaluation often result in problems concerning comparability and reproducibility of scientific and technological results. In many cases material data are recorded, but the methods of testing are insufficiently described, or such information is not communicated along with the raw data. The material data can also have numerous different formats such as paper printouts, pdfs, excel sheets or csv-files. Hence, gathering and integrating material data from different sources is challenging for potential users like materials scientists and engineers, especially if there are contradictory data where the reasons for contradictions is not clear due their vague description. In order to address these problems, data should comply to the so called „FAIR“ principle which calls for data to be findable, accessible, interoperable, and reusable (FAIR)2 and hence be accessible via so-called decentralized but interconnected data spaces. By using knowledge representation withontologies, data can be enriched with meaning and the methods of the testing procedures can be accurately provided. In this presentation we want to introduce our approach to such knowledge representation based on a high-throughput alloy development process for Cu-based alloys³ along with characterization techniques such as hardness testing and microstructural characterization (e.g. EBSD – Electron Backscattered Diffraction).
At high temperatures and in harsh environments ceramic springs are often superior to metallic ones and allow for innovative solutions. A further application was proposed by using ceramic springs as capacitive force sensor. Lower and upper coil surfaces are coated by electrically conducting layers. Deformation of such spring results in a change of capacity. Sensor application calls for helical springs with rectangular cross-section, a linear stress-strain characteristic over entire deformation range and low manufacturing tolerances relating to inner and outer diameter, coil cross section and spring pitch. Furthermore, complex spring design with integrated connecting elements has to be realized.
Alumina, zirconia (Y-TZP) and silicon nitride springs were produced by hard machining starting from sintered hollow cylinders. After external and internal cylindrical grinding the hollow cylinders were filled with hard wax, followed by multi-stage cutting of spring coils with custom-made cutting discs. Finally, hard wax was removed by melting and burnout. Best surface and edge qualities of springs were reached using Y-TZP material and hot isostatic pressed alumina. Y-TZP springs produced with material-specifically selected cutting discs and optimized process parameters show sharp coil edges without spallings and mean roughness values of inner surfaces < 0.2 μm. Manufacturing tolerances of spring diameters, spring pitch, height and width of coil cross section are in the range of ± 10 microns. Good reproducibility of spring geometry by optimized hard machining technology allows for production of Y-TZP springs with spring constants differing less than ± 1 % within a series.
According to DIN 2090 spring constant for rectangular coil cross section is proportional to the square of height and width of coil cross section and indirectly proportional to number of active coils and to the cube of the mean spring diameter. Hence, spring constants can be tailored over a range of many orders of magnitude by changing the spring dimensions. Good agreement was reached between calculated target spring constants and measured values on produced springs.
Alumina and zirconia springs were characterized relating to deformation behavior under dynamic compression load with various deformation speeds and under static tensile loads over long periods of time. Contrary to alumina springs, a non-linear stress-strain behavior of TZP springs was proved in both test series. It is supposed, that pseudoelasticity caused by stress-induced transformation of tetragonal to monoclinic phase is responsible for this special feature of TZP springs. Therefore, TZP material cannot be used for capacitive spring sensors.
Thermoelectric generators can be used as energy harvesters for sensor applications. Multilayer thermoelectric generators (ML-TEGs) are a promising alternative to conventional π-type generators due to their high filling factor, high capability of automated production and the texturing potential during the production process. Calcium cobaltite is a promising thermoelectric oxide (p-type) with highly anisotropic properties. The following study shows the development of a textured unileg ML-TEG using ceramic multilayer technology.
Tape-casting and pressure assisted sintering are applied to fabricate textured calcium cobaltite. Compared to conventional sintering, pressure assisted sintering increases the strength by the factor 10. Thermoelectric properties can be tuned either towards maximum power factor or towards maximum figure of merit depending on the pressure level.
As electrical insulation material, a screen-printable glass-ceramic with high resistivity and adapted coefficient of thermal expansion is developed. From various commercial pastes a metallization with low contact resistance is chosen. The unileg ML-TEG is co-fired in one single step. The demonstrators reach 80% of the simulated output power and the power output is highly reproducible between the different demonstrators (99%). These results provide the first proof-of-concept for fabricating co-fired multilayer generators based on textured calcium cobaltite with high power factor, high density, and high strength.
Thermal spraying provides a rapid method for additive deposition of various ceramics as electrical insulation in applications where polymers are not suitable. New applications in complex shaped additive manufactured metal parts are emerging for example in large scale electrical devices. Microstructural and dielectric evaluation of coatings is crucial to the employment of such free-form processes. The properties and microstructure of the plasma sprayed alumina coatings are compared with dense reference samples of the same powder produced by spark plasma sintering (SPS). To obtain dense bulk samples from the coarse alumina powder for spray coating, SPS is used.
Samples are fabricated by atmospheric plasma spraying (APS) of commercially available alumina powder (d50 = 33 µm) on copper substrates and by SPS of the same powder. Microstructure and porosity were analyzed by optical microscopy and scanning electron microscopy (SEM). Phase compositions were determined by X-ray diffraction (XRD). Dielectric properties such as DC resistance, dielectric strength, dielectric loss, and relative permittivity were determined according to the standards. The microstructure and dielectric properties of the coating and bulk material are compared to assess whether the coating is suitable for use in electrical insulation application.
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.
We report about a joint project aiming at the digitalization of a lab course in materials testing. The undergraduate students were asked to prepare samples of a precipitation hardened aluminum alloy and characterize them using hardness and tensile tests. In a first step, we developed the frames for the digital labor notebook using eLabFTW. The primary data and the relevant metadata of each run were saved in a central database and made available for analysis and report issues. The whole set of results produced in a course was made available in the database. This database can be improved and serve as an open repository for data on this specific alloy.
The logical frame for the joint project was provided by the PMD Core Ontology (PMDco), a mid-level ontology that enables the representation and description of processes and process chains in an MSE-specific manner, ensuring full traceability of generated data. For the digitalization of this lab course, the tensile test ontology (TTO) was applied which is designed as a module of the PMDco using strongly related semantic concepts.
The employment of renewable energy resources, which are naturally intermittent, for electricity generation has altered the working conditions of conventional power plants from continuous (baseload) to cyclic or flexible operation. For a long time, 9-12% Cr ferritic-martensitic stainless steels have been widely used in power plants due to their favorable characteristics such as high creep strength at high temperatures and oxidation and corrosion resistance. The components of power plants are subjected to long term cyclic loadings including fatigue and creep-fatigue at high temperatures. As ferritic-martensitic steels are known to exhibit cyclic softening when subjected to such loading scenarios, it is crucial to study the material response in such conditions. Since it is impossible to test the material behavior exactly as the operation conditions of power plants, due to technical difficulties and cost issues, it is necessary to develop physically-based material models that can predict the material behavior in more realistic situations.
In recent years, many material models have been proposed to describe the behavior of 9-12% Cr ferritic-martensitic stainless steels, which follow phenomenological or physically-based approaches. Phenomenological models provide a stress-strain relation based on empirical observations although they usually lack physical background. To alleviate this drawback and to allow for more flexibility and wider ranges of strain-rate and temperature, physically-based models are suggested. In this approach, microstructural evolution, dislocation movement and/or kinetics of plastic deformation processes are included. The physically-based models allow for a better extrapolation from the experiment results to other operation conditions and their material constants can be interpreted physically. Compared to the former approach, the material behavior can be described more accurately and flexibly and the number of material constants is less in general. In the presented work, the existing micromechanical models developed for P92 steel are compared and extended to allow for new dislocation-based strengthening/cyclic mechanisms. Their performance is assessed in the light of mechanical test data from creep-fatigue and thermo-mechanical fatigue experiments and detailed characterizations of the microstructure evolution in the fatigued material.
A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process.