8.0 Abteilungsleitung und andere
Filtern
Erscheinungsjahr
- 2021 (3) (entfernen)
Dokumenttyp
- Posterpräsentation (3) (entfernen)
Sprache
- Englisch (3) (entfernen)
Referierte Publikation
- nein (3) (entfernen)
Schlagworte
Organisationseinheit der BAM
The manufacturing of metal parts for the use in safety-relevant applications by Laser Powder Bed Fusion (L-PBF) demands a quality assurance of both part and process. Thermography is a nondestructive testing method that allows the in-situ determination of the thermal history of the produced part which is connected to the mechanical properties and the formation of defects [1]. A wide range of commercial thermographic camera systems working in different spectral ranges is available on the market. The understanding of the applicability of these cameras for qualitative and quantitative in-situ measurements in L-PBF is of vital importance [2]. In this study, the building process of a cylindrical specimen (Inconel 718) is monitored by three camera systems simultaniously. These camera systems are sensitive in various spectral bandwidths providing information in different temperature ranges. The performance of each camera system is explored in the context of the extraction of image features for the detection of defects. It is shown that the high temporal and thermal process dynamics are limiting factors on this matter. The combination of different spectral camera systems promises the potential of an improved defect detection by data fusion.
Environmentally friendly alternatives to cement are created through the synthesis of numerous base materials. The variation of their proportions alone leads to millions of materials candidates. Identifying suitable materials is very laborious; traditional systematic research in the laboratory consumes a lot of time and effort.
Sequential learning (SL) potentially speeds up the materials research process despite limited but highly complex available information. SL does not make direct predictions of material properties but ranks possible experiments according to their utility. The most promising experiments are prioritized over dead-end experiments and experiments whose outcome is already known.
Our work has shown that SL seems to be promising for cement research. So far, research has mainly focused on materials whose synthesis is faster and whose material properties require less time for development or characterization (allowing many successive experiments). Contrarily, in the case of binders, SL is only useful if few experiments lead to the desired goal, as for example, the determination of the compressive strength alone typically requires 28 days.
In research practice, experimental designs and the availability of resources often determine which data can be used - for example, when some laboratory resources are not available or deemed irrelevant to a task. As a result, new research scenarios are constantly emerging, each of which requires to demonstrate SL’s performance.
We are presenting the SLAMD app to facilitate the exploration of SL methods in numerous research scenarios. The app provides flexible and low-threshold access to AI methods via intuitive and interactive user interfaces. We deliberately pursue a software-based research approach (as opposed to code-, or script-based). On the one hand, the results are more comprehensible since we refer to a common (code) basis (’reproducible science’). On the other hand, the methods are easily accessible to all which accelerates the knowledge transfer into laboratory practice.
A joint project of partners from industry and research institutions for the research and construction of an analysis system for an automated, sensor-supported sorting of construction and demolition waste will be presented. This is intended to supplement or replace the previously practiced manual sorting, which harbors many risks and dangers for the staff and only enables obvious, visually detectable differences for separation. The method of laser-induced breakdown spectroscopy is to be used in combination with hyperspectral sensors. Due to the jointly processed information (data fusion), this should lead to a significant improvement in the separation of types. In addition to the sorting of different materials (concrete, main masonry building materials, organic components, glass, etc.), impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.) could also be detected and separated.
The subsequent recycling and sales opportunities are examined, such as the use of recycled aggregates in concrete, the recycling of building materials containing sulphate as a gypsum substitute for the cement industry or the agglomeration of synthetic lightweight aggregates for lightweight concrete or as a substrate for green roofs. At the same time, it is investigated whether soluble components (sulfates, heavy metals, etc.) can be detected by LIBS without a wet chemical analysis and what impact the recycling materials have on the environment.
The entire value chain is examined using the example of the Berlin location in order to minimize economic / technological barriers and obstacles on a cluster level and to sustainably increase the recovery and recycling rates.