TY - CONF A1 - Mrkwitschka, Paul A1 - Mieller, Björn A1 - Rabe, Torsten A1 - Markötter, Henning ED - Petzow, G. ED - Mücklich, F. T1 - Machine learning assisted characterization of a Low Temperature Cofired Ceramic (LTCC) module measured by synchrotron computed tomography T2 - Sonderbände der Praktischen Metallographie zur 54. Metallographie-Tagung N2 - The 5G technology promises real time data transmission for industrial processes, autonomous driving, virtual and augmented reality, E-health applications and many more. The Low Temperature Co-fired Ceramics (LTCC) technology is well suited for the manufacturing of microelectronic components for such applications. Still, improvement of the technology such as further miniaturization is required. This study focuses on the characterization of inner metallization of LTCC multilayer modules, especially on the vertical interconnect access (VIA). Critical considerations for this characterization are delamination, pore clustering in and at the edge of the VIA, deformation, and stacking offset. A LTCC multilayer consisting of a glassy crystalline matrix with silver based VIAs was investigated by synchrotron x-ray tomography (CT). The aim of this study is to propose a multitude of structural characteristic values to maximize the information gained from the available dataset. Data analysis has been done with the open source software ImageJ as well as several additional plugins. The high-resolution CT data was evaluated through 2D slices for accessibility reasons. The segmentation of all 2000 slices to assess the different regions e.g. pores, silver and glass ceramic was done by a supervised machine learning algorithm. A quantitative evaluation of shape, deformation, and porosity of the VIA with respect to its dimensions is presented and the suitability of the characterization approach is assessed. T2 - 54. Metallographie Taagung CY - Online meeting DA - 16.09.2020 KW - Machine Learning KW - LTCC KW - Synchrotron Tomography PY - 2020 SN - 978-3-88355-422-8 VL - 54 SP - 136 EP - 141 PB - Deutsche Gesellschaft für Materialkunde e.V CY - Sankt Augustin AN - OPUS4-51298 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mrkwitschka, Paul A1 - Mieller, Björn A1 - Rabe, Torsten A1 - Markötter, Henning T1 - Machine learning assisted characterization of a Low Temperature Co-fired Ceramic (LTCC) module measured by synchrotron computed tomography. N2 - The 5G technology promises real time data transmission for industrial processes, autonomous driving, virtual and augmented reality, E-health applications and many more. The Low Temperature Co-fired Ceramics (LTCC) technology is well suited for the manufacturing of microelectronic components for such applications. Still, improvement of the technology such as further miniaturization is required. This study focuses on the characterization of inner metallization of LTCC multilayer modules, especially on the vertical interconnect access (VIA). Critical considerations for this characterization are delamination, pore clustering in and at the edge of the VIA, deformation, and stacking offset. A LTCC multilayer consisting of a glassy crystalline matrix with silver based VIAs was investigated by synchrotron x-ray tomography (CT). The aim of this study is to propose a multitude of structural characteristic values to maximize the information gained from the available dataset. Data analysis has been done with the open source software ImageJ as well as several additional plugins. The high-resolution CT data was evaluated through 2D slices for accessibility reasons. The segmentation of all 2000 slices to assess the different regions e.g. pores, silver and glass ceramic was done by a supervised machine learning algorithm. A quantitative evaluation of shape, deformation, and porosity of the VIA with respect to its dimensions is presented and the suitability of the characterization approach is assessed. T2 - 54. Metallographie Tagung CY - Online meeting DA - 16.09.2020 KW - Machine Learning KW - LTCC KW - Synchrotron Tomography PY - 2020 AN - OPUS4-51299 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Geyler, Paul A1 - Rabe, Torsten A1 - Mieller, Björn A1 - Léonard, Fabien T1 - Machine learning assisted evaluation of the shape of VIAs in a LTCC multilayer N2 - The introduction of the 5G technology and automotive radar applications moving into higher frequency ranges trigger further miniaturization of LTCC technology (low temperature co-fired ceramics). To assess dimensional tolerances of inner metal structures of an industrially produced LTCC multilayer, computer tomography (CT) scans were evaluated by machine learning segmentation. The tested multilayer consists of several layers of a glass ceramic substrate with low resistance silver-based vertical interconnect access (VIA). The VIAs are punched into the LTCC green tape and then filled with silver-based pastes before stacking and sintering. These geometries must abide by strict tolerance requirements to ensure the high frequency properties. This poster presents a method to extract shape and size specific data from these VIAs. For this purpose, 4 measurements, each containing 3 to 4 samples, were segmented using the trainable WEKA segmentation, a non-commercial machine learning tool. The dimensional stability of the VIA can be evaluated regarding the edge-displacement as well as the cross-sectional area. Deviation from the ideal tubular shape is best measured by aspect ratio of each individual layer. The herein described method allows for a fast and semi-automatic analysis of considerable amount of structural data. This data can then be quantified by shape descriptors to illustrate 3-dimensional information in a concise manner. Inter alia, a 45 % periodical change of cross-sectional area is demonstrated. T2 - DKG Jahrestagung 2019 CY - Leoben, Austria DA - 06.05.2019 KW - Machine Learning KW - LTCC multilayer KW - 5G PY - 2019 AN - OPUS4-48289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Darvishi Kamachali, Reza T1 - Grain Boundary Segregation Design using CALPHAD-integrated Phase-Field Modelling N2 - A main source of current challenges in materials science and engineering is the ever-increasing complexity in materials chemistry and processing resulting in complex microstructures, making the assessment of process-microstructure-property-performance relations difficult, even unmanageable. Here the computational materials science is facing the same situation. In this talk, I share a viewpoint that the complexities in chemistry, processing and microstructures can be circumscribed by integrating existing knowledges of bulk thermodynamics and kinetics to the unknown thermodynamics and kinetics of microstructure elements. To this end, I discuss several successful examples on grain boundary segregation engineering how this scientific advance can be conducted. A roadmap is proposed, beginning to form on generalizing the concept of phase diagrams. T2 - ICAMS Advance Discussions: Advanced models for microstructure evolution – process-microstructure-property relationships CY - Bochum, Germany DA - 26.10.2022 KW - Microstructure Design KW - CALPHAD KW - Phase-Field Simulations KW - Machine Learning KW - Phase Diagrams PY - 2022 AN - OPUS4-56348 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 - Schmies, Lennart A1 - Bettge, Dirk A1 - Yarysh, Anna A1 - Sonntag, U. A1 - Botsch, B. A1 - Hemmleb, M. T1 - Using Machine Learning and Topographic SEM Imaging for Software Assisted Fractography N2 - The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. In the IGF project "iFrakto", software is being developed that quantitatively determines fracture characteristics and fracture mechanisms utilizing digitized expert knowledge, machine learning, and standard 2D and topographical data from SEM imaging. Topographical data are obtained from 4QBSE detector using shape-from-shading technology. In the medium term, a software tool should provide knowledge-based suggestions for the evaluation of fracture surfaces in real time during SEM work or at subsequent evaluation. As a basis for this, round robins were carried out among fractographers in order to create a knowledge base, to query the practice-relevant requirements for such tools and to carry out first practical tests. Actual results are presented and the relevance of the evaluation strategy is evaluated. T2 - Material Science and Engineering 2022 CY - Darmstadt, Germany DA - 27.9.2022 KW - Fractography KW - Machine Learning KW - Topography PY - 2022 AN - OPUS4-55938 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hemmleb, M. A1 - Bettge, Dirk A1 - Schmies, Lennart A1 - Sonntag, U. A1 - Botsch, B. T1 - Integrated topographic SEM imaging for software assisted fractography N2 - The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. Machine learning methods enables the quantitative determination of fracture characteristics and fracture mechanisms utilizing digitized expert knowledge [1]. Although the application of SE images provides promising results, additional information is required to obtain reliable solutions. As expected, BSE and 3D information helps to improve the classification (Fig. 1). But only a fast, widely integrated, and automated topography measurement can provide the required amount of referenced surface data for the application of machine learning methods. To fulfil these requirements, topographical data are obtained from a BSE detector with four symmetric segments (4Q-BSE) using shape-from-shading technology [2]. Surface height calculation is performed live during image acquisition and provides immediate feedback in three dimensions. All available signals (SE, BSE and more if applicable) are recorded simultaneously together with the surface topography and stored in a multichannel data file. This guaranties the same geometrical reference for all data, which is required for further analysis (Fig. 2). When applying machine learning methods to topographic data together with SEM images, topographic information must be provided as depth image. Consequently, a unique height scale is required for all applied data with different magnifications. This requires a calibrated height measurement, which is ensured with the integrated 3D calibration of the topographic acquisition and a dedicated calibration sample. Thus, a large number of data sets from different fracture samples was generated and used as training data for machine learning. T2 - 16th Multinational Congress on Microscopy CY - Brno, Czech Republic DA - 04.09.2022 KW - Fractography KW - Machine Learning KW - Topography PY - 2022 AN - OPUS4-55937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sonntag, U. A1 - Botsch, B. A1 - Bettge, Dirk A1 - Schmies, Lennart A1 - Yarysh, Anna A1 - Hemmleb, M. T1 - Softwaregestützte Bestimmung von Bruchmechanismen und Bruchflächenmerkmalen mittels Machine Learning und Topographie-Informationen N2 - Ziel von fraktographischen Untersuchungen ist die Auswertung von makroskopischen und mikroskopischen Bruchflächenmerkmalen und daraus resultierend die Bestimmung des Bruchmechanismus eines Bauteils aus einem Schadensfall. Die Grundlage für die Bewertung von Bruchmerkmalen stammt dabei aus zuvor durchgeführten eigenen Vergleichsversuchen und aus der Literatur. Eine fraktographische Analyse kann sehr aufwändig sein und setzt in jedem Fall erhebliches Erfahrungswissen voraus. Im IGF-Vorhaben „iFrakto“ wird Software entwickelt, die auf Basis von digitalisiertem Expertenwissen, Machine Learning und unter Zuhilfenahme von Topographie-Daten anhand von REM-Aufnahmen Bruchmerkmale und Bruchmechanismen quantitativ bestimmt, s. Abbildung. Solche Software soll mittelfristig direkt am REM oder bei der nachfolgenden Auswertung wissensbasierte Vorschläge für die Bewertung von Bruchflächen liefern. Als Grundlage hierfür wurden Ringversuche unter Fraktograph/inn/en durchgeführt, um die Wissensbasis zu schaffen, die praxisrelevanten Anforderungen an solche Software abzufragen und erste Praxistests durchzuführen. Die bisherigen Ergebnisse werden vorgestellt und die Relevanz der Auswertestrategie und unterschiedlicher Sensordaten bewertet. T2 - 56. Metallographie-Tagung CY - Saarbrücken, Germany DA - 21.9.2022 KW - Fraktographie KW - Machine Learning KW - Topographie PY - 2022 AN - OPUS4-55939 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bettge, Dirk A1 - Schmies, Lennart T1 - Die Fraktographische Online-Datenbank der AG Fraktographie – Entwicklungsstand und weitere Planung N2 - Vortrag zum aktuellen Stand der fraktographischen online-Datenbank. Darstellung des AGM/DVM Gemeinschaftsausschusses REM in der Materialforschung und seiner Arbeitsgruppen Fraktographie, EBSD und 2D/3D-Prüfung. Ziele und Inhalte der fraktographischen Datenbank, Aufruf zum Mitmachen. Durchführung von Vergleichsversuchen, Entwicklung einer fraktographischen Symbolik, Ringversuch, Vorhaben iFrakto mit Entwicklung einer KI-gestützten Bruchflächenanalyse. T2 - Metallographietagung 2023 CY - Leoben, Austria DA - 13.09.2023 KW - Fraktographie KW - Machine Learning KW - Datenbank PY - 2023 AN - OPUS4-58467 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schmies, Lennart A1 - Sonntag, U. A1 - Bettge, Dirk A1 - Le, Quynh-Hoa A1 - Yarysh, Anna A1 - Botsch, B. T1 - Quantitative Fraktographie unter Verwendung klassischer Verfahren, Topographie-Daten und Deep-Learning N2 - Die Analyse von Bruchflächen wird in der Schadensanalyse meist auf der Basis von Erfahrungswissen vorgenommen, welches aus vorliegenden Untersuchungen, eigenen Vergleichsversuchen und aus der Literatur stammt. Durch Vergleiche mit bereits vorliegenden Bildern werden qualitativ Bruchmechanismen ermittelt. Grundlage sind zumeist zweidimensionale Bilder aus licht- und elektronenoptischen Verfahren. Quantitative Aussagen beziehen sich bislang beispielsweise auf makroskopische Anteile von Bruchmechanismen oder die Ausmessung von Schwingstreifen. In jüngerer Zeit gibt es vermehrt Ansätze, Computer-Algorithmen einzusetzen, die in der Lage sind, unterschiedlich strukturierte Bruchmerkmale zu finden und zu klassifizieren. Im hier vorgestellten IGF-Vorhaben „iFrakto“, IGF Vorhaben Nr.: 21477 N, werden licht- und elektronen-optisch Topographie-Bilder erzeugt und die gewonnenen 3D-Informationen zusammen mit den klassischen 2D-Bildern ausgewertet. Analytische Algorithmen und Machine Learning werden eingesetzt, um Bruchmerkmale zu analysieren, zu bewerten und mit Informationen aus einer fraktographischen Datenbank zu verknüpfen. Ziel ist die Bereitstellung von Software zur Unterstützung der Fraktographie in der Schadensanalyse. In diesem Beitrag werden erste Ergebnisse des Vorhabens vorgestellt. T2 - 55. Metallographie-Tagung CY - Online meeting DA - 29.9.2021 KW - Fraktographie KW - Machine Learning KW - Datenbank KW - REM KW - Topographie PY - 2021 AN - OPUS4-53491 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -