@misc{58325, author = {{Autsch, Sabiene}}, publisher = {{Edition Imorde }}, title = {{{Micro Archives. Künstlerische Arbeiten 2019-2024. }}}, year = {{2024}}, } @inproceedings{52216, author = {{Tews, Karina and Teutenberg, Dominik and Meschut, Gerson}}, booktitle = {{DECHEMA Workshop für Klebstoffanwender: Simulation von Klebverbindungen}}, location = {{Köln}}, title = {{{Mechanisches Verhalten: Charakterisierung von Klebstoffen}}}, year = {{2024}}, } @article{58409, author = {{Kattenstroth, Fiona and Disselkamp, Jan-Philipp and Lick, Jonas and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, pages = {{442--447}}, publisher = {{Elsevier BV}}, title = {{{Challenges in the implementation of simulation models for the digital factory twin - a systematic literature review}}}, doi = {{10.1016/j.procir.2024.07.052}}, volume = {{128}}, year = {{2024}}, } @unpublished{58441, abstract = {{This study presents a numerical approach using a 3D finite element model to quantify the remaining clamp load of a plastic nut joint after a specific time. The viscoelastic relaxation of a thermoplastic nut, which is predominantly screwed on a welding stud, is described by a material card using Prony Series. Prony Series are derived from experimental Dynamical Mechanical Analysis with different moisture and fiber contents of the thermoplastic. Since plastic nuts usually do not have preformed threads, the increased temperatures and resulting stresses from the thread-forming process are considered in the simulation. Firstly, the FE model is verified by substrate stress relaxation tests. Subsequently, experimental clamp load measurements with miniature compression load cells verify the clamp load prediction. Finally, the developed model is used to analyze the clamp load distribution within the threads}}, author = {{Wippermann, Jan and Meschut, Gerson}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Numerical modeling of clamp load relaxation of plastic nuts under varying moisture and fiber content}}}, year = {{2024}}, } @phdthesis{58448, abstract = {{Die Inbetriebnahme von Steuerungen und Regelungen stellt sicher, dass ein mechatronisches System ordnungsgemäß funktioniert und den Anforderungen gerecht wird. Der modellbasierte Entwurf basiert auf einem genauen Simulationsmodell. Allerdings ist dieser klassische Weg bei komplexen Systemen oft nicht praktikabel, da die analytische Modellierung zu kompliziert und zeitaufwendig ist. Diese Forschungslücke wird durch Verfahren adressiert, die eine effiziente und sichere Inbetriebnahme ermöglichen. Diese Verfahren kombinieren Regelungstechnik und Reinforcement Learning und nutzen vorhandenes Wissen über die Regelungsaufgabe, um Korrekturen basierend auf Messdaten und der probabilistischen Gauß-Prozess-Regression vorzunehmen. Das Vorwissen kann als teilweise bekanntes physikalisches Modell oder als Steuerungsfunktion vorliegen. Anwendungsbeispiele sind der Ultraschalldrahtbondprozess, verschiedene Pendelsysteme und ein Hexapod. Eine angepasste Bayessche Optimierung wird zur Identifikation einer Steuerparametrisierung für das Ultraschallbonden eingesetzt. Außerdem wird eine hybride Optimalsteuerung für das Doppelpendel auf einem Wagen entwickelt und erfolgreich validiert. Fur einen Hexapod zur Fahrzeugachsprüfung wird eine hybride Zustandslinearisierung formuliert und ein Funktionsnachweis im Rahmen einer Simulation erbracht. Die Einhaltung technischer Rahmenbedingungen und stabiles Systemverhalten werden durch probabilistische Pradiktionen gewährleistet. In allen Anwendungsfällen wird eine Steigerung der Effizienz und Güte erzielt.}}, author = {{Hesse, Michael}}, isbn = {{978-3-947647-45-3}}, issn = {{2365-4422}}, publisher = {{Heinz Nixdorf Institut}}, title = {{{Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression}}}, doi = {{10.17619/UNIPB/1-2135}}, volume = {{426}}, year = {{2024}}, } @article{58491, abstract = {{Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.}}, author = {{Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert, Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}}, issn = {{2504-4494}}, journal = {{Journal of Manufacturing and Materials Processing}}, keywords = {{ductile damage, stress-state dependency, failure, parameter identification, punch test, clinching}}, number = {{4}}, publisher = {{MDPI AG}}, title = {{{Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining}}}, doi = {{10.3390/jmmp8040157}}, volume = {{8}}, year = {{2024}}, } @inproceedings{59237, abstract = {{Batch and process fluctuations during the fabrication of sheet metal components result in discrepancies in the resulting component properties, affecting subsequent process steps and potentially leading to production rejects. Consequently, the identification of deviations and knowledge of the effects of fluctuations are crucial for achieving consistently high product quality, reducing waste and thus increasing resource efficiency of production processes through countermeasures derived from this. The approach presented to address this is the use of data-driven metamodeling to map entire process chains and predict process parameters in order to compensate for process and batch fluctuation. The investigated process chain consists of the sub-processes deep drawing, clamping and clinching. For each process step, relevant input and output variables are identified, numerical simulation models are created, and subsequently validated. Variant simulations of the sub-processes are conducted and evaluated to generate a database for the metamodeling of the individual process steps. Machine learning techniques are utilized for the automated selection and optimization of learning methods to create models that depict the relationships between input and output variables. Finally, the models for the sub-processes are linked together to form a superordinate metamodel for the entire process chain, with the aim to make inline-process adaptations possible.Abstract Purpose Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ($$\dot{V}$$ V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict $$\dot{V}$$ V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for $$\dot{V}$$ V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of $$\dot{V}$$ V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion Machine learning models predict $$\dot{V}$$ V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number DRKS00031401 (6 March 2023, retrospectively registered). }}, author = {{Wenzel, Charlotte and Liebig, Thomas and Swoboda, Adrian and Smolareck, Rika and Schlagheck, Marit Lea and Walzik, David and Groll, Andreas and Goulding, Richie P. and Zimmer, Philipp}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, number = {{11}}, pages = {{3421--3431}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features}}}, doi = {{10.1007/s00421-024-05543-x}}, volume = {{124}}, year = {{2024}}, } @misc{12950, author = {{Claes, Leander and Webersen, Manuel}}, publisher = {{GitHub, Inc.}}, title = {{{pyfds 0.3.1 - modular field simulation tool}}}, doi = {{10.5281/ZENODO.2649826}}, year = {{2024}}, } @article{60176, abstract = {{AbstractAimTo investigate the associations of the Dietary Approaches to Stop Hypertension (DASH) score with subcutaneous (SAT) and visceral (VAT) adipose tissue volume and hepatic lipid content (HLC) in people with diabetes and to examine whether changes in the DASH diet were associated with changes in these outcomes.MethodsIn total, 335 participants with recent‐onset type 1 diabetes (T1D) and type 2 diabetes (T2D) from the German Diabetes Study were included in the cross‐sectional analysis, and 111 participants in the analysis of changes during the 5‐year follow‐up. Associations between the DASH score and VAT, SAT and HLC and their changes were investigated using multivariable linear regression models by diabetes type. The proportion mediated by changes in potential mediators was determined using mediation analysis.ResultsA higher baseline DASH score was associated with lower HLC, especially in people with T2D (per 5 points: −1.5% [−2.7%; −0.3%]). Over 5 years, a 5‐point increase in the DASH score was associated with decreased VAT in people with T2D (−514 [−800; −228] cm3). Similar, but imprecise, associations were observed for VAT changes in people with T1D (−403 [−861; 55] cm3) and for HLC in people with T2D (−1.3% [−2.8%; 0.3%]). Body mass index and waist circumference changes explained 8%‐48% of the associations between DASH and VAT changes in both groups. In people with T2D, adipose tissue insulin resistance index (Adipo‐IR) changes explained 47% of the association between DASH and HLC changes.ConclusionsA shift to a DASH‐like diet was associated with favourable VAT and HLC changes, which were partly explained by changes in anthropometric measures and Adipo‐IR.}}, author = {{Schaefer, Edyta and Lang, Alexander and Kupriyanova, Yuliya and Bódis, Kálmán B. and Weber, Katharina S. and Buyken, Anette and Barbaresko, Janett and Kössler, Theresa and Kahl, Sabine and Zaharia, Oana‐Patricia and Szendroedi, Julia and Herder, Christian and Schrauwen‐Hinderling, Vera B. and Wagner, Robert and Kuss, Oliver and Roden, Michael and Schlesinger, Sabrina}}, issn = {{1462-8902}}, journal = {{Diabetes, Obesity and Metabolism}}, number = {{10}}, pages = {{4281--4292}}, publisher = {{Wiley}}, title = {{{Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated with lower visceral and hepatic lipid content in recent‐onset type 1 diabetes and type 2 diabetes}}}, doi = {{10.1111/dom.15772}}, volume = {{26}}, year = {{2024}}, } @inproceedings{64104, author = {{Scheideler, Christian and Hinnenthal , Kristian and Liedtke, David Jan}}, title = {{{Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20}}}, year = {{2024}}, } @inproceedings{64106, author = {{Scheideler, Christian and Kostitsyna, Irina and Liedtke, David Jan}}, title = {{{Universal Coating by 3D Hybrid Programmable Matter.}}}, year = {{2024}}, } @inproceedings{52211, author = {{Beule, Felix and Teutenberg, Dominik and Meschut, Gerson}}, booktitle = {{DECHEMA-Workshop für Klebstoffanwender: Simulation von Klebverbindungen}}, location = {{Köln}}, title = {{{Klebstoffmodell - Parameteridentifikation, Verifikation und Validierung für den Lastfall Crash}}}, year = {{2024}}, } @inproceedings{52214, author = {{Beule, Felix and Teutenberg, Dominik and Meschut, Gerson and Schmelzle, Lars and Possart, Gunnar and Mergheim, Julia and Steinmann, Paul}}, location = {{Köln}}, title = {{{Methodenentwicklung zur Simulation von hyperelastischen Klebverbindungen unter Crashbelastung}}}, year = {{2024}}, } @article{62073, abstract = {{ A numerical modelling strategy for the direct pin pressing process of metallic pins into continuous fibre-reinforced thermoplastic organosheets is developed. The joining process is performed above the thermoplast’s melting temperature, altering the initial material structure of the composite by fibre rearrangement, which in turn influences the load-bearing capacity of the joint. Therefore, the modelling strategy aims at predicting the resultant material structure after pin pressing. The modelling approach considers both the textile architecture and the process parameters (temperature, tool velocity). A sub-meso modelling framework for the fibres based on a multi-filament approach is used. The interaction between fibres and the thermoplastic melt, as well as the matrix flow, is modelled using the Arbitrary Lagrangian Eulerian method. This allows for the prediction of matrix-rich zones and fibre rearrangement around the pin. The promising results show a good agreement of the resultant material structure in terms of compaction and fibre volume content around the pressed pin. Characteristic parameters show an underestimation of the laminate thickness below the pin. Moreover, an evaluation method for evaluating the orientation changes of the virtual multi-filaments is developed and presented to observe and assess fibre rearrangement and fibre volume content in detail during the numerical process simulation. It can be seen that only fibres around the pin are displaced and not in the whole molten area. Furthermore, it can be observed in detail that the initial position of the fibres in relation to the pin determines whether the fibres are displaced in the in-plane or out-of-plane direction. }}, author = {{Gröger, B. and Gerritzen, Johannes and Hornig, A. and Gude, M.}}, issn = {{1464-4207}}, journal = {{Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications}}, number = {{12}}, pages = {{2286--2298}}, publisher = {{SAGE Publications}}, title = {{{Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining}}}, doi = {{10.1177/14644207241280035}}, volume = {{238}}, year = {{2024}}, } @inproceedings{55638, abstract = {{Abstract. Traditionally, joints are cylindrical and rotationally symmetric. In the present study, non-rotationally symmetric joints are used for joining steel and Glass mat-reinforced thermoplastic sheets (GMT). In addition, the study also analyzes the impact of non-rotational symmetric joint rotation on the load-bearing capacity. Single lap joint specimens were fabricated using the In-Mold assembly technique for joining steel sheets with GMT. Tensile shear tests were performed on different orientations of the joint geometry, and it was observed that changing the joint orientation influences the load-bearing capacity. The joints are constitutively modeled using beam elements and the influence of joint rotation on load distribution is examined through a static simulation study. }}, author = {{Devulapally, Deekshith Reddy and Martin, Sven and Tröster, Thomas}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Non-rotationally symmetric joints – Mechanisms and load bearing capacity}}}, doi = {{10.21741/9781644903131-183}}, year = {{2024}}, } @article{64858, abstract = {{Simulation models are used to design extruders in the polymer processing industry. This eliminates the need for prototypes and reduces development time for extruders and, in particular, extrusion screws. These programs simulate, among other process parameters, the temperature and pressure curves in the extruder. At present, it is not possible to predict the resulting melt quality from these results. This paper presents a simulation model for predicting the melt quality in the extrusion process. Previous work has shown correlations between material and thermal homogeneity and the screw performance index. As a result, the screw performance index can be used as a target value for the model to be developed. The results of the simulations were used as input variables, and with the help of artificial intelligence—more precisely, machine learning—a linear regression model was built. Finally, the correlation between the process parameters and the melt quality was determined, and the quality of the model was evaluated.}}, author = {{Trienens, Dorte and Schöppner, Volker and Krause, Peter and Bäck, Thomas and Tsi-Nda Lontsi, Seraphin and Budde, Finn}}, issn = {{2073-4360}}, journal = {{Polymers}}, number = {{9}}, publisher = {{MDPI AG}}, title = {{{Method Development for the Prediction of Melt Quality in the Extrusion Process}}}, doi = {{10.3390/polym16091197}}, volume = {{16}}, year = {{2024}}, } @article{56190, abstract = {{This study investigates the potential of using advanced conversational artificial intelligence (AI) to help people understand complex AI systems. In line with conversation-analytic research, we view the participatory role of AI as dynamically unfolding in a situation rather than being predetermined by its architecture. To study user sensemaking of intransparent AI systems, we set up a naturalistic encounter between human participants and two AI systems developed in-house: a reinforcement learning simulation and a GPT-4-based explainer chatbot. Our results reveal that an explainer-AI only truly functions as such when participants actively engage with it as a co-constructive agent. Both the interface’s spatial configuration and the asynchronous temporal nature of the explainer AI – combined with the users’ presuppositions about its role – contribute to the decision whether to treat the AI as a dialogical co-participant in the interaction. Participants establish evidentiality conventions and sensemaking procedures that may diverge from a system’s intended design or function.}}, author = {{Klowait, Nils and Erofeeva, Maria and Lenke, Michael and Horwath, Ilona and Buschmeier, Hendrik}}, journal = {{Discourse & Communication}}, number = {{6}}, pages = {{917--930}}, publisher = {{Sage}}, title = {{{Can AI explain AI? Interactive co-construction of explanations among human and artificial agents}}}, doi = {{10.1177/17504813241267069}}, volume = {{18}}, year = {{2024}}, } @inproceedings{61350, author = {{Massopo, Orlando and Schmid, Hans-Joachim and Reddemann, Manuel and Kneer, Reinhold and Bieber, Malte}}, publisher = {{6th International Symposium Gas-Phase Synthesis of Functional Nanomaterials: Fundamental Understanding, Modeling and Simulation, Scale-up and Application}}, title = {{{Influence of Dispersion Gas and Resulting Reaction Zone on the Particle Formation in Spray Flame Synthesis (Presentation)}}}, year = {{2024}}, }