VP.2 Informationstechnik
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- OpenBIS (2)
- PBF-LB/M (2)
- Porosity prediction (2)
Organisationseinheit der BAM
- VP Vizepräsident (7)
- VP.2 Informationstechnik (7)
- 8 Zerstörungsfreie Prüfung (3)
- 8.3 Thermografische Verfahren (3)
- VP.1 eScience (3)
- 4 Material und Umwelt (2)
- 4.2 Material-Mikrobiom Wechselwirkungen (2)
- 8.5 Röntgenbildgebung (2)
- 1 Analytische Chemie; Referenzmaterialien (1)
- 1.0 Abteilungsleitung und andere (1)
This study presents a method for predicting the emissivity of rough cast iron surfaces to enhance the accuracy of laser thermography in industrial non-destructive testing (NDT). Traditional emissivity measurements are impractical in industrial settings due to environmental interference and equipment limitations. The proposed approach replaces complex setups like integrating spheres with a simplified illumination unit and reflection measurements. By leveraging known material properties and surface characteristics, an algorithm predicts directional emissivity. Experimental results of an initial feasibility study show strong agreement between predicted and measured values, demonstrating the method’s potential for real-time emissivity correction in industrial thermographic inspections.
Background: CWS/RMS‐96 was an international multicenter trial with randomization between two therapy arms of the standard four‐drug therapy (vincristine, ifosfamide, adriamycin, dactinomycin [VAIA]) versus an intensified six‐drug regimen (carboplatin, epirubicin, vincristine, dactinomycin, ifosfamide, and etoposide [CEVAIE]) for high‐risk rhabdomyosarcoma (RMS), extraskeletal Ewing sarcoma (EES), and undifferentiated sarcoma (UDS) in children, adolescents, and young adults aiming to improve their survival. Intensified chemotherapy with CEVAIE did not improve outcome.
Methods: Patients younger than 21 years with a previously untreated localized HR‐RMS, EES, and UDS were enrolled from Cooperative Weichteilsarkom Studiengruppe (CWS) centers in Germany, Austria, Poland, Switzerland, and from Italian Soft Tissue Sarcoma Committee (STSC) centers. Randomization (1:1) to receive either 9 × 21 days cycles of VAIA or CEVAIE was performed separately in CWS and STSC. Hyperfractionated accelerated radiotherapy (32–44.8 Gy) was added at week 9–12 according to histology and response to chemotherapy. A secondary microscopically complete nonmutilating resection was performed if possible. Primary endpoints were response to chemotherapy, event‐free (EFS) and overall survival (OS).
Results: Five hundred fifty‐seven patients (HR‐RMS:n = 416, EES and UDS:n = 141) underwent randomization: VAIA (n = 273) or CEVAIE (n = 284). Radiotherapy was given to 70% of patients in both groups. A secondary resection was performed in 47% and 48% patients, respectively. The 5‐year EFS and OS for the VAIA and CEVAIE treatment arms were 59.8% and 60.8% (p = .89), and 74.2% and 68.3% (p = .16), respectively. No differences in response, toxicity, or second malignancies emerged in the two groups.
Conclusion: The use of an intensified regimen failed to show a significant improvement in tumor response and outcome of patients with localized HR‐RMS, EES, and UDS.
The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data.
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.
In view of the increasing digitization of research and the use of data-intensive measurement and analysis methods, research institutions and their staff are faced with the challenge of documenting a constantly growing volume of data in a comprehensible manner, archiving them for the long term, and making them available for discovery and re-use by others in accordance with the FAIR principles. At BAM, we aim to facilitate the integration of research data management (RDM) strategies during the whole research cycle from the creation and standardized description of materials datasets to their publication in open repositories. To this end, we present the BAM Data Store, a central system for internal RDM that fulfills the heterogenous demands of materials science and engineering labs. The BAM Data Store is based on openBIS, an open-source software developed by the ETH Zurich that has originally been created for life science laboratories but that has since been deployed in a variety of research domains. The software offers a browser-based user interface for the digital representation of lab inventory entities (e.g., samples, chemicals, instruments, and protocols) and an electronic lab notebook for the standardized documentation of experiments and analyses.
To investigate whether openBIS is a suitable framework for the BAM Data Store, we carried out a pilot phase during which five research groups with employees from 16 different BAM divisions were introduced to the software. The pilot groups were chosen to represent a diverse array of domain use cases and RDM requirements (e.g., small vs big data volume, heterogenous vs structured data types) as well as varying levels of prior IT knowledge on the users’ side.
Overall, the results of the pilot phase are promising: While the creation of custom data structures and metadata schemas can be time-intensive and requires the involvement of domain experts, the system offers specific benefits in the form of a simplified documentation and automation of research processes, as well as constituting a basis for data-driven analysis. In this way, heterogeneous research workflows in various materials science research domains could be implemented, from the synthesis and characterization of nanomaterials to the monitoring of engineering structures. In addition to the technical deployment and the development of domain-specific metadata standards, the pilot phase also highlighted the need for suitable institutional infrastructures, processes, and role models. An institute-wide rollout of the BAM Data Store is currently being planned.
