Chemie und Prozesstechnik
Filtern
Dokumenttyp
- Zeitschriftenartikel (171) (entfernen)
Sprache
- Englisch (119)
- Deutsch (50)
- Mehrsprachig (1)
- Russisch (1)
Referierte Publikation
- nein (171) (entfernen)
Schlagworte
- Nanoparticles (9)
- Non-destructive testing (9)
- Concrete (6)
- Electron microscopy (6)
- Archaeometry (5)
- Inks (5)
- KorroPad (5)
- Korrosionsschnelltest (5)
- Metrology (5)
- Nichtrostender Stahl (5)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (58)
- 8 Zerstörungsfreie Prüfung (48)
- 6 Materialchemie (44)
- 6.1 Oberflächen- und Dünnschichtanalyse (20)
- 7 Bauwerkssicherheit (16)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (16)
- 4 Material und Umwelt (14)
- 6.3 Strukturanalytik (14)
- 8.0 Abteilungsleitung und andere (14)
- 1.1 Anorganische Spurenanalytik (12)
In situ chemical analysis of duplex stainless steel weld by laser induced breakdown spectroscopy
(2024)
The high corrosion resistance and good mechanical properties of duplex stainless steel (DSS) are due to its special chemical composition, which is a balanced phase ratio of ferrite (α) and austenite (γ). Many industrial applications require the integration of DSS components. For this, Gas tungsten arc welding (GTAW) is an excellent choice, as it allows an automated operation with high reproducibility. However, when the weld pool solidifies, critical ratios of α- and γ- phases can occur, which lead to solidification cracking, increased susceptibility to corrosion, and a decrease in ductility and critical strength. Previous studies have shown that these defects can be caused by the accumulation of manganese and chromium in the heat affected zone (HAZ), requiring ongoing monitoring of this accumulation. A suitable method for such monitoring is laser-induced breakdown spectroscopy (LIBS), which can be used in two operating modes: calibration using standard reference samples and calibration-free. Unlike conventional quantitative LIBS measurements, which require reference samples to generate a calibration curve, calibration-free LIBS (CF-LIBS) allows chemical compositions to be determined solely from the emission spectrum of the plasma. Numerous publications show that CF-LIBS is a fast and efficient analytical method for the quantitative analysis of metal samples. In this work, CF-LIBS is applied to spectra obtained during GTAW DSS welding and the result is compared with those obtained by PLS analysis. A good correlation was found between both types of analysis, demonstrating the suitability of the CF-LIBS method for this application. The CF-LIBS method has a significant advantage over conventional LIBS due to the rapid in situ measurement of concentrations of major alloying elements without calibration procedure. This, combined with fast feedback and appropriate adjustment of welding parameters, helps prevent welding defects.
Technische Regeln im Arbeitsschutz – wie TRBS für die Betriebssicherheitsverordnung und TRGS für die Gefahrstoffverordnung – sind nicht zwingendes Recht, sondern konkretisierende Empfehlungen mit Vermutungswirkung. In Gerichtsurteilen werden sie eher selten herangezogen. Wie diese – und Branchenregelungen von Verbänden – von der Rechtsprechung diskutiert werden, zeigt ein Urteil des Verwaltungsgerichts Münster.
The BAM Data Store
(2023)
As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities.
The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis.
Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.
Angesichts der zunehmenden Digitalisierung und dem Einsatz datenintensiver Methodiken in der Wissenschaft stehen Forschende vor der Herausforderung, stetig wachsende Datenmengen nachvollziehbar zu dokumentieren, langfristig zu speichern und für Dritte nachnutzbar zu machen. Um diesen Anforderungen gerecht zu werden, bietet sich die Nutzung von Software-Lösungen an, welche Forschungsdatenmanagement mit der digitalen Dokumentation von Laborinventar und Experimenten in elektronischen Laborbüchern (engl. electronic lab notebooks (ELN)) verknüpfen.
The processes of hydrogen reduction of silicon and germanium chlorides under the conditions of high-frequency (40.68 MHz) counteracted arc discharge stabilized between two rod electrodes are investigated. The main gas-phase and solid products of plasma-chemical transformations are determined. Thermodynamic analysis of SiCl4 + H2 and GeCl4 + H2 systems for optimal process parameters was carried out. Using the example of hydrogen reduction of SiCl4 by the method of numerical modeling, gas-dynamic and thermal processes for this type of discharge are investigated. The impurity composition of gas-phase and solid reaction products is investigated. The possibility of single-stage production of high-purity Si and Ge mainly in the form of compact ingots, as well as high-purity chlorosilanes and trichlorogermane, is shown.
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.