Ingenieurwissenschaften und zugeordnete Tätigkeiten
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- 2022 (12) (entfernen)
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- Automation (3)
- Bonding analysis (3)
- High-throughput computations (3)
- Aging (2)
- Aluminium alloy (2)
- Creep (2)
- Al2CuMg (1)
- Alkali activated concrete (1)
- Analyses (1)
- Automated synthesis (1)
- Brinell hardness (1)
- Charpy test (1)
- Concrete (1)
- Creep-fatigue (1)
- DFT (1)
- Data format (1)
- Data management (1)
- Datasets (1)
- Digital representation (1)
- Dwell-Fatigue (1)
- EN AW 2618A a (1)
- EN AW-2618A (1)
- MOUSE (1)
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- Mechanical testing (1)
- Microstructure analysis (1)
- Nanoparticles (1)
- Phonons (1)
- Radii distribution (1)
- S-phase (1)
- S355 steel sheet (1)
- Saxs (1)
- Scientific software (1)
- Sequential learning (1)
- Silver (1)
- Stress relaxation (1)
- Structure prediction (1)
- TEM (1)
- Tempered martensite-ferritic steel (1)
- Tensile test (1)
- Waxs (1)
- X-ray scattering (1)
Organisationseinheit der BAM
Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology).
WEBSLAMD
(2022)
The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future.
1. Summary
Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact.
Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution.
Workflow
Digital Lab
Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources
Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations.
Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization.
AI-Optimization
Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements.
SLAMD-FIB-Case-Study
(2022)
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders part of the energy-intensive clinker production process can be dispensed with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement formulations can significantly accelerate research. The „Sequential Learning App for Materials Discovery“ (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative cements and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
SI Files for "Towards automation of the polyol process for the synthesis of silver nanoparticles"
(2022)
The graphml file: reaction_graph_AgNP.graphml is included. It contains topological information (Fig. 1 in the main text) about the reaction setup and metadata with reaction condtions. It used by the Python API used to control the Chemputer.
SAXS reports. The complete report sheets generated by McSAS are included. They contain extended information characterising the size distributions and the fitting parameters.
NP3_I: saxs_report_NP3_I.pdf
NP3_II: saxs_report_NP3_II.pdf
NP3_III: saxs_report_NP3_III.pdf
NP3_IV: saxs_report_NP3_IV.pdf
NP5_I: saxs_report_NP5_I.pdf
NP5_II: saxs_report_NP5_II.pdf
NP5_III: saxs_report_NP5_III.pdf
The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.
The data were generated in the frame of the digitization project Innovationplatform MaterialDigital (PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.
The dataset contains experimental mechanical data from complex service-like creep-fatigue experiments performed isothermally at 620 °C and a low strain amplitude of 0.2 % on tempered martensite-ferritic grade P92 steel. The data sets in text file format provide cyclic deformation (min. and max. stresses) and the total (hysteresis) data of all recorded fatigue cycles for three different creep-fatigue experiments: 1) a standard relaxation fatigue (RF) test with symmetrical dwell times of three minutes introduced at minimum and maximum strain, 2) a fully strain-controlled service-like relaxation (SLR) test combining these three-minute peak strain dwells with a 30-minute dwell in between at zero strain, and 3) a partly stress-controlled service-like creep (SLC) test combining the three-minute peak strain dwells with 30-minute dwells at constant stress.
Further information on data and data acquisition, analysis, and experimental details are given in “Experimental Data from Service-Like Creep-Fatigue Experiments on Grade P92 Steel”, submitted to Data in Brief. Additional analyses of these datasets, as well as experimental findings and discussions are presented in “Creep-Fatigue of P92 in Service-Like Tests with Combined Stress- and Strain-Controlled Dwell Times”, submitted to International Journal of Fatigue.