9 Komponentensicherheit
Filtern
Erscheinungsjahr
- 2022 (29) (entfernen)
Dokumenttyp
- Vortrag (16)
- Zeitschriftenartikel (9)
- Posterpräsentation (3)
- Sonstiges (1)
Schlagworte
- Additive Manufacturing (10)
- AGIL (9)
- Residual Stress (6)
- Neutron diffraction (5)
- Additive Fertigung (4)
- Additive manufacturing (4)
- Thermography (4)
- X-ray and Neutron Diffraction (4)
- Laser powder bed fusion (3)
- Stainless Steel (3)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (29) (entfernen)
Eingeladener Vortrag
- nein (16)
Laser powder bed fusion (PBF-LB/M) of metallic alloys is a layer wise additive manufacturing process which provides significant scope for more efficient designs of components, benefiting performance and weight, leading to efficiency improvements for various sectors of industry. However, to benefit from these design freedoms, knowledge of the high produced induced residual stress and mechanical property anisotropy associated with the unique microstructures is critical. X-ray and neutron diffraction are considered the benchmark for non-destructive characterization of surface and bulk internal residual stress. The latter, characterized by the high penetration power in most engineering alloys, allows for the use of diffraction angle close to 90° enabling a near cubic sampling volume to be specified. However, the complex microstructures of columnar growth with inherent crystallographic texture typically produced during PBF-LB/M of metallics present significant challenges to the assumptions typically required for time efficient determination of residual stress. These challenges include the selection of an appropriate set of diffraction elastic constants and a representative strain-free reference for the material of interest. In this presentation advancements in the field of diffraction-based residual stress analysis of L-PBF Inconel 718 will be presented. The choice of an appropriate set of diffraction-elastic constants depending on the underlying microstructure will be described.
Ein Umlaufkühler ist im Betrieb explodiert. Splitter des zerborstenen Gehäuses aus Kunststoff wurden mit dem Kühlwasser in die Umgebung geschleudert, am Betriebsort entstand Personenschaden. Bei Funktionsprüfungen am beschädigten Gerät traten unerwartet - aber reproduzierbar - Knalleffekte bei Berührung der Außenoberfläche der Kupfer-Kühlschlange auf. Ein möglicher Mechanismus konnte im Labor durch Synthese von Kupferazid auf Kupferproben und Auslösung vergleichbarer Knalleffekte nachgestellt werden. Damit ist die Plausibilität des beschriebenen Schadensereignisses mit diesem oder einem ähnlich reagierenden Stoff belegt. Ein eindeutiger Nachweis darüber, dass bei dem aufgetretenen Schadensfall dieselbe chemische Reaktion stattgefunden hat, war nicht möglich, da die Belag-Überreste aus dem explodierten Kühlgerät für eine Analyse nicht mehr in ausreichender Menge verfügbar gewesen sind.
Im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz haben DIN und DKE im Januar 2022 die Arbeiten an der zweiten Ausgabe der Deutschen Normungsroadmap Künstliche Intelligenz gestartet. In einem breiten Beteiligungsprozess und unter Mitwirkung von mehr als 570 Fachleuten aus Wirtschaft, Wissenschaft, öffentlicher Hand und Zivilgesellschaft wurde damit der strategische Fahrplan für die KI-Normung weiterentwickelt. Koordiniert und begleitet wurden diese Arbeiten von einer hochrangigen Koordinierungsgruppe für KI-Normung und -Konformität.
Mit der Normungsroadmap wird eine Maßnahme der KI-Strategie der Bundesregierung umgesetzt und damit ein wesentlicher Beitrag zur „KI – Made in Germany“ geleistet.
Die Normung ist Teil der KI-Strategie und ein strategisches Instrument zur Stärkung der Innovations- und Wettbewerbsfähigkeit der deutschen und europäischen Wirtschaft. Nicht zuletzt deshalb spielt sie im geplanten europäischen Rechtsrahmen für KI, dem Artificial Intelligence Act, eine besondere Rolle.
Influence of a 265 °C heat treatment on the residual stress state of a PBF-LB/M AlSi10Mg alloy
(2022)
Laser Powder Bed Fusion (PBF-LB/M) additive manufacturing (AM) induces
high magnitude residual stress (RS) in structures due to the extremely heterogeneous cooling and heating rates. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their generation and evolution after post-process heat treatments. In this study, one of the few of its kind, the RS relaxation induced in an as-built PBF-LB/M AlSi10Mg material by a low-temperature heat treatment (265 °C for 1 h) is studied by means of X-ray and neutron diffraction. Since the specimens are manufactured using a baseplate heated up to 200 °C, low RS are found in the as-built condition. After heat treatment a redistribution of the RS is observed, while their magnitude remains constant. It is proposed that the redistribution is induced by a repartition of stresses between the a-aluminium matrix and the silicon phase, as the morphology of the silicon phase is affected by the heat treatment. A considerable scatter is observed in the neutron diffraction RS profiles, which is principally correlated to the presence (or absence) of pockets of porosity developed at the borders of the chessboard pattern.
Additive manufacturing methods such as laser powder bed fusion (LPBF) allow geometrically complex parts to be manufactured within a single step. However, as an aftereffect of the localized heat input, the rapid cooling rates are the origin of the large residual stress (RS) retained in as-manufactured parts. With a view on the microstructure, the rapid directional cooling leads to a cellular solidification mode which is accompanied by columnar grown grains possessing crystallographic texture. The solidification conditions can be controlled by the processing parameters and the scanning strategy. Thus, the process allows one to tailor the microstructure and the texture to the specific needs. Yet, such microstructures are not only the origin of the mechanical anisotropy but also pose metrological challenges for the diffraction-based RS determination. In that context the micromechanical elastic anisotropy plays an important role: it translates the measured microscopic strain to macroscopic stress. Therefore, it is of uttermost importance to understand the influence of the hierarchical microstructures and the texture on the elastic anisotropy of LPBF manufactured materials.
This study reveals the influence of the build orientation and the texture on the micro-mechanical anisotropy of as-built Inconel 718. Through variations of the build orientation and the scanning strategy, we manufactured specimens possessing [001]/[011]-, [001]-, and [011]/[111]-type textures. The resulting microstructures lead to differences in the macroscopic mechanical properties. Even further, tensile in-situ loading experiments during neutron diffraction measurements along the different texture components revealed differences in the microstrain response of multiple crystal lattice planes. In particular, the load partitioning and the residual strain accumulation among the [011]/[111] textured specimen displayed distinct differences measured up to a macroscopic strain of 10 %. However, the behavior of the specimens possessing [001]/[011]-and [001]-type texture was only minorly affected. The consequences on the metrology of RS analysis by diffraction-based methods are discussed.
The appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived.
Competence Centre H2Safety@BAM – Safeguarding the Quality Infrastructure in the Hydrogen Economy
(2022)
Hydrogen is a central component of the energy transition and the European Green Deal for a climate-neutral Europe. To achieve the goals defined for 2050, the EU and the German government have developed a framework for action and are making long-term investments in research, development, and the rapid implementation of innovative hydrogen technologies.
With the Competence Centre "H2Safety@BAM", BAM is creating the safety-related prerequisites for the successful implementation of hydrogen technologies at national as well as European level.
To this end, BAM conducts research, testing and consulting in a holistic and inter-disciplinary manner under one roof – in the Competence Centre H2Safety@BAM. The activities of the competence centre focus on ensuring the safety of infra-structures, plants, and processes as well as innovative hydrogen transport and storage systems based on digital quality testing and the experimental testing of components and systems. It thus creates the prerequisites for the implementation and adaptation of legal framework conditions as well as for standardization. Closely related to this are investigations into the properties and compatibility of metallic materials and polymers as well as friction systems for the safe operation of components, plants, processes, and systems. This interdisciplinary and holistic approach is complemented by the development of gas analytics and suitable sensors, online measurements of gas properties in process control, test scenarios under real conditions as well as impact assessments of accidents and risk assessments and risk management systems derived from them. With its portfolio of tasks and competences, BAM builds trust in the safety and reliability of technical system solutions for hydrogen.
Al-Si alloys produced by Laser Powder Bed Fusion (PBF-LB/M) techniques allow the fabrication of lightweight free-shape components. Due to the extremely heterogeneous cooling and heating, PBF-LB/M induces high magnitude residual stress (RS) and a fine Si microstructure. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their evolution before and after post-process heat treatments (HT).
The formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The LPBF process is characterized by a large number of influencing factors which can be hard to quantify. Machine Learning (ML) is a prominent tool to predict the outcome of complex processes on the basis of different sensor data. In this study, a ML model for defect prediction is created using thermographic image features as input data. As a reference, the porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan of the produced specimen is used. Physical knowledge about the keyhole pore formation is incorporated into the model to increase the prediction accuracy. From the prediction result, the quality of the input data is evaluated and future demands on in-situ monitoring of LPBF processes are formulated.