TY - CONF A1 - Traub, Heike T1 - Application of different calibration strategies for the analysis of pure copper using nano- and femtosecond laser ablation ICP-MS T2 - European Winter Conference on Plasma Spectrochemistry CY - Graz, Austria DA - 2009-02-15 PY - 2009 AN - OPUS4-19007 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Matschat, Ralf T1 - Application of different analytical methods for the multi-element ultra-trace dertermination in ultrahigh purity metals T2 - 14th Conference on Ultrahigh Purity Metals CY - Paris, France DA - 2007-07-02 PY - 2007 AN - OPUS4-14947 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hickel, Tilmann T1 - Application of Density Functional Theory in the Context of Phase Diagram Modelling N2 - An important pillar for the modelling of phase diagrams is the availability of accurate thermodynamic data for the individual phases. While experimental calorimetry is a powerful technique, a prediction of materials behavior solely on the computer, i.e. complementary to experimental investigations, has its own advantages. It allows one to resolve uncertainties in experimental data, to develop physical thermodynamic models and to determine free energies of phases that are not stable in reality. For this purpose, density functional theory (DFT), which does not require any experimental or empirical fit parameters, is the method of choice. Its advantage is to take the quantum-mechanical solution for the electronic wave functions as a starting point, when determining the chemical bonding of atoms. In the first part of the talk, I will speak about enthalpies of formation resulting from this approach, the underlying approximations, the achievable accuracies, as well as the typical incorporation into Calphad assessments. We note, however, that a disadvantage of DFT is the restriction of the method to T = 0 K in their original formulation. Phase stabilities of real materials, on the other hand, are often dominated by processes that are due to finite temperatures. To include temperature effects in DFT, a significant method development was necessary in the last decade. In the second part of this talk, I will therefore provide the basic concepts for the calculation of vibrational, electronic, and magnetic entropy contributions. I will also highlight some recent methodological achievements in this field and show how these methods opened new routes in understanding and characterizing materials. At the end a few examples of ab initio based phase diagrams will be discussed. The focus will be on phase stabilities and microstructure formation in Ni-based materials. The advantages of combining theoretical, experimental and Calphad concepts will be particularly emphasized. T2 - MSIT Winter School on Materials Chemistry CY - Tegernsee, Germany DA - 21.01.2024 KW - Ab initio simulations KW - CALPHAD KW - Thermodynamics KW - Free energies PY - 2024 AN - OPUS4-62729 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hickel, Tilmann T1 - Application of density functional theory in the context of phase diagram modelling N2 - In this presentation the fundamentals of density functional theory are explained in the context of phase diagram modelling. A special focus is on the Ni-Mg phase diagram. T2 - 7th MSIT Winter School CY - Kreuth, Germany DA - 12.03.2023 KW - Ab initio simulations KW - Thermodynamics KW - Phase diagrams PY - 2023 AN - OPUS4-58855 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Evsevleev, Sergei T1 - Application of deep learning to the segmentation of synchrotron X-ray tomography data of multiphase metal matrix composites N2 - The 3D microstructure of an Al alloy matrix composite with two ceramic reinforcements was investigated by synchrotron X-ray tomography. A deep learning algorithm was used for the segmentation of four different phases. We show that convolutional networks with the U-Net architecture are able to solve complex segmentation tasks with small amount of training data. T2 - International Conference on Tomography of Materials & Structures CY - Cairns, Australia DA - 22.07.2019 KW - Synchrotron X-ray tomography KW - Deep learning KW - Segmentation KW - Metal matrix composite PY - 2019 AN - OPUS4-48606 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Elfetni, Seif A1 - Darvishi Kamachali, Reza T1 - Application of deep learning to multi-phase-field modelling and simulation N2 - Recent advances in Deep Learning (DL) have significantly impacted the field of materials modelling. DL tools have been recently considered as promising tools to address the complex relationships among processing, microstructure and property of materials. The thermal stability of polycrystalline materials is a highly interesting and complex problem that could be addressed using DL techniques. The Multi-Phase-Field (MPF) method has emerged as a powerful tool for addressing grain growth phenomena from multiple perspectives. Unlike sharp-interface based methods, the MPF approach bypasses the need for detailed information on individual grains. In this work, we use DL to address issues related to MPF simulations of grain growth including numerical efficiency, computing speed, and resource consumption. This presents specific challenges for high-performance computing (HPC) due to the large datasets and complex computations required by both MPF and DL methods. We study various 3D microstructure settings with the goal of accelerating the simulation process while exploring different physical effects. In particular, the impact of grain boundary and triple junction energies on grain growth are to be investigated. The results will be presented in terms of the evolving size and shape distribution of the grains. T2 - Euromat 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Phase-Field Simulation KW - Microstructure Evolution KW - Physics-informed Neural Network KW - Machine Learning PY - 2023 AN - OPUS4-58225 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir T1 - Application of deep learning for structural health monitoring of a composite overwrapped pressure vessel undergoing cyclic loading N2 - Structural health monitoring (SHM) using ultrasonic-guided waves (UGWs) enables continuous monitoring of components with complex geometries and provides extensive information about their structural integrity and their overall condition. Composite overwrapped pressure vessels (COPVs) used for storing hydrogen gases at very high pressures are an example of a critical infrastructure that could benefit significantly from SHM. This can be used to increase the periodic inspection intervals, ensure safe operating conditions by early detection of anomalies, and ultimately estimate the remaining lifetime of COPVs. Therefore, in the digital quality infrastructure initiative (QI-Digital) in Germany, an SHM system is being developed for COPVs used in a hydrogen refueling station. In this study, the results of a lifetime fatigue test on a Type IV COPV subjected to many thousands of load cycles under different temperatures and pressures are presented to demonstrate the strengths and challenges associated with such an SHM system. During the cyclic testing up to the final material failure of the COPV, a sensor network of fifteen surface-mounted piezoelectric (PZT) wafers was used to collect the UGW data. However, the pressure variations, the aging process of the COPV, the environmental parameters, and possible damages simultaneously have an impact on the recorded signals. This issue and the lack of labeled data make signal processing and analysis even more demanding. Thus, in this study, semi-supervised, and unsupervised deep learning approaches are utilized to separate the influence of different variables on the UGW data with the final aim of detecting and localizing the damage before critical failure. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Anomaly detection KW - Damage localization KW - Deep learning KW - Structural health monitoring KW - Ultrasonic-guided waves PY - 2024 AN - OPUS4-60745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heideklang, René T1 - Application of Data Fusion in Nondestructive Testing (NDT) T2 - 16th International Conference on Information Fusion CY - Istanbul, Turkey DA - 2013-07-09 PY - 2013 AN - OPUS4-29780 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Steckert, Carsten T1 - Application of CRT displays and scanners for correct colour image reproduction of naturally weathered paint coatings T2 - DFZ-BAM-Workshop: Digital and analog ISO-Test chart reproduction in offices CY - Berlin, Germany DA - 1999-11-01 PY - 1999 AN - OPUS4-4822 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Nolde, Moana T1 - Application of AUTODYN at BAM T2 - ANSYS AUTODYN Update Seminar 2009 CY - Freiburg, Germany DA - 2009-05-19 PY - 2009 AN - OPUS4-19589 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -