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- 8.0 Abteilungsleitung und andere (3) (entfernen)
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AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material's pore space. Specific NMR features are then integrated into the material design model, with the model's performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating that NMR-based, low-fidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures.
Related work
Laboratory Study:
Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors
https://doi.org/10.3390/app11198820
On-Site Study:
TBA
Doctoral Thesis:
Non-destructive classification of moisture deterioration in layered building floors using ground penetrating radar
https://doi.org/10.14279/depositonce-19306
Measurement Parameters
The GPR measurements were carried out with the SIR 20 from GSSI and a 2 GHz antenna pair (bandwidth 1 GHz to 3 GHz) in common-offset configuration. Each B-Scan consists of N A-Scans, each including 512 samples of a 11 ns time window. Survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan No Gains were applied.
Folder Description:
Lab_dry, Lab_insulDamage, Lab_screedDamage
- each contain 168 Measurements (B-Scans) in .csv on 84 dry floors, floors with insulation damage and screed damage.
- each floor setup was measured twice on two orthogonal survey lines, indicated by _Line1_ and _Line2_ in the file name.
- the file names encode the building floor setup e.g. CT50XP100 describes a 50 mm cement screed with 100 mm extruded polystyrene below
- the material codes are
CT: cement screed, CA: anhydrite screed, EP: expanded polystyrene, XP: extruded polystyrene, GW: glass wool, PS: perlites
further information can be found in the publication https://doi.org/10.3390/app11198820
OnSite_
- 5 folders containing B-Scans on 5 different practical moisture damages
- the building floor setup is encoded according to the lab with an additional measurement point numbering at the start and a damage case annotation at the end of the file name with _dry, _insulationDamage and_screedDamage
File Description:
B-Scans, Measurement files - no header
- dimension: 512 x N data point with N beeing the number of A-Scans including 512 samples of a 11 ns time window.
- survey lines were recorded with 250 A-Scans/ meter, which equals a 4 mm spacing between each A-Scan
Moisture References
- Moist_Reference of On-Site Locations include the columns MeasPoint: Measurement point, wt%Screed: moisture content of screed layer in mass percent; wt%Insul: moisture content of insulation layer in mass percent. References were obtained by drilling cores with 68 mm diameter in the center of each survey line.
- Moist_Reference_Screed of Lab data include the columns Screed: Screed material and thickness in mm, wt%Screed moisture content of screed layer in mass percent
- Moist Reference_Insul of Lab data include the columns Insulation: Insulation material and thickness in mm, water addition in l: water added to the insulation layer in liters, V%Insulation: water added to the insulation layer in volume percent, RH%: resulting relative humidy in the insulation layer during measurement. These References are only avaible for Lab measurements on insulation damages.