@article{44890, author = {{Tchomgue Simeu, Arnold and Mahnken, Rolf}}, issn = {{1617-7061}}, journal = {{PAMM}}, keywords = {{Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics}}, number = {{1}}, publisher = {{Wiley}}, title = {{{Goal‐oriented adaptivity based on a model hierarchy of mean‐field and full‐field homogenization methods in elasto‐plasticity}}}, doi = {{10.1002/pamm.202200053}}, volume = {{22}}, year = {{2023}}, } @article{41492, abstract = {{The current investigation shows the feasibility of 316L steel powder production via three different argon gas atomisation routes (closed coupled atomisation, free fall atomisation with and without hot gas), along with subsequent sample production by laser powder bed fusion (PBF-LB). Here, a mixture of pure Fe and atomised 316L steel powder is used for PBF-LB to induce a chemical composition gradient in the microstructure. Optical microscopy and μ-CT investigations proved that the samples processed by PBF-LB exhibit very little porosity. Combined EBSD-EDS measurements show the chemical composition gradient leading to the formation of a local fcc-structure. Upon heat treatment (1100 °C, 14 h), the chemical composition is homogeneous throughout the microstructure. A moderate decrease (1060 to 985 MPa) in the sample’s ultimate tensile strength (UTS) is observed after heat treatment. However, the total elongation of the as-built and heat-treated samples remains the same (≈22%). Similarly, a slight decrease in the hardness from 341 to 307 HV1 is observed upon heat treatment.}}, author = {{Pramanik, Sudipta and Andreiev, Anatolii and Hoyer, Kay-Peter and Krüger, Jan Tobias and Hengsbach, Florian and Kircheis, Alexander and Zhao, Weiyu and Fischer-Bühner, Jörg and Schaper, Mirko}}, issn = {{2674-0516}}, journal = {{Powders}}, number = {{1}}, pages = {{59--74}}, publisher = {{MDPI AG}}, title = {{{Powder Production via Atomisation and Subsequent Laser Powder Bed Fusion Processing of Fe+316L Steel Hybrid Alloy}}}, doi = {{10.3390/powders2010005}}, volume = {{2}}, year = {{2023}}, } @article{43441, abstract = {{This paper reveals the 3D character of the intermetallic layer at the aluminum–steel interface which pops up above the original sample surface during annealing. Popping out of the intermetallics was proven using atomic force microscopy. The phase expands out of the plane due to the exothermic formation of the Al5Fe2 phase and the feasibility of surface diffusion. Milling by a focused ion beam enabled the comparison of the chemical composition of the surface layer with the bulk interface, showing no difference. The growth direction is both towards aluminum and steel — the main diffusion flux is from aluminum towards steel, and the new intermetallic phase emerges at the steel side. The shortage of Al atoms causes a shift of the intermetallic as a whole towards aluminum.}}, author = {{Šlapáková, Michaela and Kihoulou, Barbora and Veselý, Jozef and Minárik, Peter and Fekete, Klaudia and Knapek, Michal and Králík, Rostislav and Grydin, Olexandr and Stolbchenko, Mykhailo and Schaper, Mirko}}, issn = {{0042-207X}}, journal = {{Vacuum}}, keywords = {{Al-steel clad, twin-roll casting, 3D characterization, atomic force microscopy, diffusion direction, surface growth}}, publisher = {{Elsevier BV}}, title = {{{3D-structure of intermetallic interface layer in Al–steel clad material}}}, doi = {{10.1016/j.vacuum.2023.112043}}, volume = {{212}}, year = {{2023}}, } @article{44078, author = {{Andreiev, Anatolii and Hoyer, Kay-Peter and Hengsbach, Florian and Haase, Michael and Tasche, Lennart and Duschik, Kristina and Schaper, Mirko}}, issn = {{0924-0136}}, journal = {{Journal of Materials Processing Technology}}, keywords = {{Industrial and Manufacturing Engineering, Metals and Alloys, Computer Science Applications, Modeling and Simulation, Ceramics and Composites}}, publisher = {{Elsevier BV}}, title = {{{Powder bed fusion of soft-magnetic iron-based alloys with high silicon content}}}, doi = {{10.1016/j.jmatprotec.2023.117991}}, volume = {{317}}, year = {{2023}}, } @article{45557, author = {{Rossel, Moritz Sebastian and Meschut, Gerson}}, journal = {{BLECHE+ROHRE+PROFILE}}, publisher = {{Meisenbach Verlag }}, title = {{{Prozesskettensimulation für das Clinchen mit beweglicher Matrize}}}, volume = {{02/2023}}, year = {{2023}}, } @article{45663, author = {{Schmolke, Tobias and Brunner-Schwer, Christian and Biegler, Max and Rethmeier, Michael and Meschut, Gerson}}, journal = {{Journal of Manufacturing and Materials Processing}}, number = {{3}}, publisher = {{MDPI}}, title = {{{On Welding of High-Strength Steels Using Laser Beam Welding and Resistance Spot Weld Bonding with Emphasis on Seam Leak Tightness }}}, doi = {{10.3390/jmmp7030116}}, volume = {{7}}, year = {{2023}}, } @article{45757, abstract = {{AbstractThree prominent low order implicit time integration schemes are the first order implicit Euler-method, the second order trapezoidal rule and the second order Ellsiepen method. Its advantages are stability and comparatively low computational cost, however, they require the solution of a nonlinear system of equations. This paper presents a general approach for the construction of third order Runge–Kutta methods by embedding the above mentioned implicit schemes into the class of ELDIRK-methods. These will be defined to have an Explicit Last stage in the general Butcher array of Diagonal Implicit Runge–Kutta (DIRK) methods, with the consequence, that no additional system of equations must be solved. The main results—valid also for non-linear ordinary differential equations—are as follows: Two extra function calculations are required in order to embed the implicit Euler-method and one extra function calculation is required for the trapezoidal-rule and the Ellsiepen method, in order to obtain the third order properties, respectively. Two numerical examples are concerned with a parachute with viscous damping and a two-dimensional laser beam simulation. Here, we verify the higher order convergence behaviours of the proposed new ELDIRK-methods, and its successful performances for asymptotically exact global error estimation of so-called reversed embedded RK-method are shown. }}, author = {{Mahnken, Rolf}}, issn = {{0178-7675}}, journal = {{Computational Mechanics}}, keywords = {{Applied Mathematics, Computational Mathematics, Computational Theory and Mathematics, Mechanical Engineering, Ocean Engineering, Computational Mechanics}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Derivation of third order Runge–Kutta methods (ELDIRK) by embedding of lower order implicit time integration schemes for local and global error estimation}}}, doi = {{10.1007/s00466-023-02347-2}}, year = {{2023}}, } @article{45782, abstract = {{The development of automotive components with reduced greenhouse gas (GHG) emissions is needed to reduce overall vehicle emissions. Life Cycle Engineering (LCE) based on Life Cycle Assessment (LCA) supports this by providing holistic information and improvement potentials regarding eco-efficient products. Key factors influencing LCAs of automotive components, such as material production, will change in the future. First approaches for integrating future scenarios for these key factors into LCE already exist, but they only consider a limited number of parameters and scenarios. This work aims to develop a method that can be practically applied in the industry for integrating prospective LCAs (pLCA) into the LCE of automotive components, considering relevant parameters and consistent scenarios. Therefore, pLCA methods are further developed to investigate the influence of future scenarios on the GHG emissions of automotive components. The practical application is demonstrated for a vehicle component with different design options. This paper shows that different development paths of the foreground and background system can shift the ecological optimum of design alternatives. Therefore, future pathways of relevant parameters must be considered comprehensively to reduce GHG emissions of future vehicles. This work contributes to the methodological and practical integration of pLCA into automotive development processes and provides quantitative results.}}, author = {{Grenz, Julian and Ostermann, Moritz and Käsewieter, Karoline and Cerdas, Felipe and Marten, Thorsten and Herrmann, Christoph and Tröster, Thomas}}, issn = {{2071-1050}}, journal = {{Sustainability}}, keywords = {{prospective LCA, life cycle engineering (LCE), lightweight design, automotive components, body parts, circular economy, steel, aluminum, hybrid materials, fiber metal laminates}}, number = {{13}}, publisher = {{MDPI AG}}, title = {{{Integrating Prospective LCA in the Development of Automotive Components}}}, doi = {{10.3390/su151310041}}, volume = {{15}}, year = {{2023}}, } @inproceedings{33734, abstract = {{Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis}}, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)}}, editor = {{Pesquita, Catia and Jimenez-Ruiz, Ernesto and McCusker, Jamie and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Raphael and Hertling, Sven}}, keywords = {{Neural network, Concept learning, Description logics}}, location = {{Hersonissos, Crete, Greece}}, pages = {{209 -- 226}}, publisher = {{Springer International Publishing}}, title = {{{Neural Class Expression Synthesis}}}, doi = {{https://doi.org/10.1007/978-3-031-33455-9_13}}, volume = {{13870}}, year = {{2023}}, } @inbook{45829, author = {{Keller, Adrian and Grundmeier, Guido}}, booktitle = {{Reference Module in Chemistry, Molecular Sciences and Chemical Engineering}}, isbn = {{9780124095472}}, publisher = {{Elsevier}}, title = {{{High-speed AFM studies of macromolecular dynamics at solid/liquid interfaces}}}, doi = {{10.1016/b978-0-323-85669-0.00123-9}}, year = {{2023}}, } @phdthesis{44323, abstract = {{Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.}}, author = {{Kersting, Joschka}}, pages = {{208}}, publisher = {{Universität der Bundeswehr München }}, title = {{{Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining}}}, year = {{2023}}, } @inproceedings{45877, author = {{Brunner-Schwer, Christian and Biegler, Max and Rethmeier, Michael and Schmolke, Tobias and Meschut, Gerson and Spohr, Sebastian and Eckstein, Lutz}}, location = {{Munich}}, title = {{{A life cycle assessment of joining processes in the automotive industry, illustrated by the example of an EV battery case}}}, year = {{2023}}, } @inbook{45882, author = {{Bäumer, Frederik Simon and Chen, Wei-Fan and Geierhos, Michaela and Kersting, Joschka and Wachsmuth, Henning}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{65--84}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation}}}, doi = {{10.5281/zenodo.8068456}}, volume = {{412}}, year = {{2023}}, } @article{46138, abstract = {{This work reports a fully guided setup for single-mode squeezing on integrated titanium-indiffused periodically poled nonlinear resonators. A continuous-wave laser beam is delivered and the squeezed field is collected by single-mode fibers; up to −3.17(9) dB of useful squeezing is available in fibers. To showcase the usefulness of such a fiber-coupled device, we applied the generated squeezed light in a fiber-based phase sensing experiment, showing a quantum enhancement in the signal-to-noise ratio of 0.35 dB. Moreover, our investigation of the effect of photorefraction on the cavity resonance condition suggests that it causes system instabilities at high powers.}}, author = {{Domeneguetti, Renato and Stefszky, Michael and Herrmann, Harald and Silberhorn, Christine and Andersen, Ulrik L. and Neergaard-Nielsen, Jonas S. and Gehring, Tobias}}, issn = {{0146-9592}}, journal = {{Optics Letters}}, keywords = {{Atomic and Molecular Physics, and Optics}}, number = {{11}}, publisher = {{Optica Publishing Group}}, title = {{{Fully guided and phase locked Ti:PPLN waveguide squeezing for applications in quantum sensing}}}, doi = {{10.1364/ol.486654}}, volume = {{48}}, year = {{2023}}, } @inbook{46205, abstract = {{We present a concept for quantifying evaluative phrases to later compare rating texts numerically instead of just relying on stars or grades. We achievethis by combining deep learning models in an aspect-based sentiment analysis pipeline along with sentiment weighting, polarity, and correlation analyses that combine deep learning results with metadata. The results provide new insights for the medical field. Our application domain, physician reviews, shows that there are millions of review texts on the Internet that cannot yet be comprehensively analyzed because previous studies have focused on explicit aspects from other domains (e.g., products). We identify, extract, and classify implicit and explicit aspect phrases equally from German-language review texts. To do so, we annotated aspect phrases representing reviews on numerous aspects of a physician, medical practice, or practice staff. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and physicians’ professions (e.g., surgeon, ophthalmologist). As a result, we have individual numerical scores that contain a variety of information based on deep learning algorithms that extract textual (evaluative) information and metadata from the Web.}}, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Data Management Technologies and Applications}}, editor = {{Cuzzocrea, Alfredo and Gusikhin, Oleg and Hammoudi, Slimane and Quix, Christoph}}, isbn = {{9783031378898}}, issn = {{1865-0929}}, pages = {{45--65}}, publisher = {{Springer Nature Switzerland}}, title = {{{Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews}}}, doi = {{10.1007/978-3-031-37890-4_3}}, volume = {{1860}}, year = {{2023}}, } @inproceedings{43228, abstract = {{The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parameters, here within the Intel oneAPI tool flow, to create customized designs for 256 different ERI quartet classes, based on their orbitals. To maximize data reuse, all intermediates are buffered in FPGA on-chip memory with customized layout. The pre-calculation of intermediates also helps to overcome data dependencies caused by multi-dimensional recurrence relations. The involved loop structures are partially or even fully unrolled for high throughput of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary bitwidth integers is integrated in the FPGA kernels. To our best knowledge, this is the first work on ERI computation on FPGAs that supports more than just the single most basic quartet class. Also, the integration of ERI computation and compression it a novelty that is not even covered by CPU or GPU libraries so far. Our evaluation shows that using 16-bit integer for the ERI compression, the fastest FPGA kernels exceed the performance of 10 GERIS ($10 \times 10^9$ ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors around $10^{-7}$ - $10^{-5}$ Hartree. The measured throughput can be accurately explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform similar computations using the widely used libint reference on a two-socket server with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x.}}, author = {{Wu, Xin and Kenter, Tobias and Schade, Robert and Kühne, Thomas and Plessl, Christian}}, booktitle = {{2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)}}, pages = {{162--173}}, title = {{{Computing and Compressing Electron Repulsion Integrals on FPGAs}}}, doi = {{10.1109/FCCM57271.2023.00026}}, year = {{2023}}, } @inproceedings{45661, abstract = {{Effect chain modelling is a method for creating information models for impact analyses of changes in system elements. For the estimation of change propagation, dependencies between requirements must be detected. The high number of require- ment dependencies in the engineering of complex technical systems results in the need for automation. In a study, it was shown that transformer models (BERT) are suitable for the automated dependency analysis of requirements. However, there are currently deficits in the applicability of the models for different projects without an extensive and heterogeneous training database. This paper investigates how active learning can be used to train BERT models (active-BERT) in order to increase the performance of the models for classifying requi- rement dependencies of projects with heterogeneous require- ments. The results show that the performance of the models increases significantly through active learning. Through active- BERT, engineers are enabled to model effect chains efficiently and to handle requirement changes effectively.}}, author = {{Gräßler, Iris and Preuß, Daniel}}, booktitle = {{Stuttgarter Symposium für Produktentwicklung SSP 2023}}, editor = {{Hölzle, Katharina and Kreimeyer, Matthias and Roth, Daniel and Maier, Thomas and Riedel, Oliver}}, issn = {{2364-4885}}, location = {{Stuttgart}}, publisher = {{Fraunhofer IAO}}, title = {{{Automatisierte Abhängigkeitsanalyse von Anforderungen zur Wirkkettenmodellierung}}}, year = {{2023}}, } @inproceedings{46502, author = {{Gräßler, Iris and Wiechel, Dominik}}, booktitle = {{2023 18th Annual System of Systems Engineering Conference (SoSe)}}, location = {{Lille}}, publisher = {{IEEE}}, title = {{{Customized impact analyses for technical engineering changes}}}, doi = {{10.1109/sose59841.2023.10178484}}, year = {{2023}}, } @article{46503, abstract = {{ Purpose The purpose of this study is to investigate the manufacturability of Fe-3Si lattice structures and the resulting mechanical properties. This study could lead to the successful processing of squirrel cage conductors (a lattice structure by design) of an induction motor by additive manufacturing in the future. Design/methodology/approach The compression behaviour of two lattice structures where struts are arranged in a face-centred cubic position and vertical edges (FCCZ), and struts are placed at body-centred cubic (BCC) positions, prepared by laser powder bed fusion (LPBF), is explored. The experimental investigations are supported by finite element method (FEM) simulations. Findings The FCCZ lattice structure presents a peak in the stress-strain curve, whereas the BCC lattice structure manifests a plateau. The vertical struts aligned along the compression direction lead to a significant increase in the load-carrying ability of FCCZ lattice structures compared to BCC lattice structures. This results in a peak in the stress-strain curve. However, the BCC lattice structure presents the bending of struts with diagonal struts carrying the major loads with struts near the faceplate receiving the least load. A high concentration of geometrically necessary dislocations (GNDs) near the grain boundaries along cell formation is observed in the microstructure. Originality/value To the best of the authors’ knowledge, this is the first study on additive manufacturing of Fe-3Si lattice structures. Currently, there are no investigations in the literature on the manufacturability and mechanical properties of Fe-3Si lattice structures. }}, author = {{Pramanik, Sudipta and Hoyer, Kay-Peter and Schaper, Mirko}}, issn = {{1355-2546}}, journal = {{Rapid Prototyping Journal}}, keywords = {{Industrial and Manufacturing Engineering, Mechanical Engineering}}, number = {{6}}, pages = {{1257--1269}}, publisher = {{Emerald}}, title = {{{Experimental and finite element method investigation on the compression behaviour of FCCZ and BCC lattice structures of additively manufactured Fe-3Si samples}}}, doi = {{10.1108/rpj-06-2022-0190}}, volume = {{29}}, year = {{2023}}, } @article{46507, author = {{Pramanik, Sudipta and Milaege, Dennis and Hein, Maxwell and Andreiev, Anatolii and Schaper, Mirko and Hoyer, Kay-Peter}}, issn = {{1438-1656}}, journal = {{Advanced Engineering Materials}}, keywords = {{Condensed Matter Physics, General Materials Science}}, number = {{14}}, publisher = {{Wiley}}, title = {{{An Experimental and Computational Modeling Study on Additively Manufactured Micro‐Architectured Ti–24Nb–4Zr–8Sn Hollow‐Strut Lattice Structures}}}, doi = {{10.1002/adem.202201850}}, volume = {{25}}, year = {{2023}}, }