@article{63611, abstract = {{When humans interact with artificial intelligence (AI), one desideratum is appropriate trust. Typically, appropriate trust encompasses that humans trust AI except for instances in which they either explicitly notice AI errors or are suspicious that errors could be present. So far, appropriate trust or related notions have mainly been investigated by assessing trust and reliance. In this contribution, we argue that these assessments are insufficient to measure the complex aim of appropriate trust and the related notion of healthy distrust. We introduce and test the perspective of covert visual attention as an additional indicator for appropriate trust and draw conceptual connections to the notion of healthy distrust. To test the validity of our conceptualization, we formalize visual attention using the Theory of Visual Attention and measure its properties that are potentially relevant to appropriate trust and healthy distrust in an image classification task. Based on temporal-order judgment performance, we estimate participants' attentional capacity and attentional weight toward correct and incorrect mock-up AI classifications. We observe that misclassifications reduce attentional capacity compared to correct classifications. However, our results do not indicate that this reduction is beneficial for a subsequent judgment of the classifications. The attentional weighting is not affected by the classifications' correctness but by the difficulty of categorizing the stimuli themselves. We discuss these results, their implications, and the limited potential for using visual attention as an indicator of appropriate trust and healthy distrust.}}, author = {{Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}}, issn = {{1664-1078}}, journal = {{Frontiers in Psychology}}, keywords = {{appropriate trust, healthy distrust, visual attention, Theory of Visual Attention, human-AI interaction, Bayesian cognitive model, image classification}}, publisher = {{Frontiers Media SA}}, title = {{{Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}}}, doi = {{10.3389/fpsyg.2025.1694367}}, volume = {{16}}, year = {{2026}}, } @inproceedings{60958, abstract = {{Large Language Models (LLMs) excel in understanding, generating, and processing human language, with growing adoption in process mining. Process mining relies on event logs that capture explicit process knowledge; however, knowledge-intensive processes (KIPs) in domains such as healthcare and product development depend on tacit knowledge, which is often absent from event logs. To bridge this gap, this study proposes a LLM-based framework for mobilizing tacit process knowledge and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs can capture tacit process knowledge through targeted queries and systematically integrate it into event logs. This study presents a novel approach combining LLMs, knowledge management, and process mining, advancing the understanding and management of KIPs by enhancing knowledge accessibility and documentation.}}, author = {{Brennig, Katharina}}, booktitle = {{AMCIS 2025 Proceedings. 11.}}, keywords = {{Process Mining, Large Language Model, Knowledge Management, Knowledge-Intensive Process, Tacit Knowledge}}, location = {{Montréal}}, title = {{{Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes}}}, year = {{2025}}, } @inproceedings{61432, abstract = {{This study investigated how action histories – unfolding sequences of actions with objects – provide a context for both attentional allocation and linguistic repair strategies. Building on theories of enactive cognition and sensorimotor contingency theory, we experimentally manipulated action sequences (action history) to create either simple or rich “situational models,” and investigated how these models interact with attention and reflect in linguistic processes during human–robot interaction. Participants (N = 30) engaged in a controlled object placement task with a humanoid robot, where the action (manner) information was either provided or omitted. The omission elicited repair behaviors in participants that were in focus of our investigation. For rich models (competing action possibilities) participants demonstrated: a) increased attentional reorientation, reflecting active engagement with the situational model b) preference for restricted repairs, targeting the specific source of trouble in action selection. Conversely, a simple situational model led to more generalized attention patterns and open repair strategies, suggesting weaker constraints on internal processing. These findings highlight how situational structures emerge externally to scaffold internal cognitive processes, with action histories serving as a crucial context for the interface between perception, action, and language. We discuss how to implement such a tight loop in the assistance of a system.}}, author = {{Singh, Amit and Rohlfing, Katharina J.}}, booktitle = {{IEEE International Conference on Development and Learning (ICDL)}}, keywords = {{Attention, Action, Repairs, Task model, HRI, Eyemovement}}, location = {{Prague}}, title = {{{Manners Matter: Action history guides attention and repair choices during interaction}}}, doi = {{10.31234/osf.io/yn2we_v1}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @article{62937, abstract = {{Sandwich packings are assembled from two conventional structured packings with different geometrical surface areas stacked alternatingly within a separation column. When operated under partially flooded conditions, they provide significant mass transfer improvement compared to common structured packings. In this work, a rate-based model including novel mass transfer correlations is presented and validated using a comprehensive experimental database for the reactive absorption of CO2 into aqueous monoethanolamine. The proposed rate-based approach is capable of accounting for axial dispersion, thereby enabling the evaluation of the effect of liquid-phase backmixing on the mass transfer performance. The validated rate-based model is used to evaluate the separation performance of sandwich packings. Compared with structured packings, up to 10 % higher mass transfer rates are obtained.}}, author = {{Franke, Patrick and Schubert, Markus and Hampel, Uwe and Kenig, Eugeny Y.}}, issn = {{0009-2509}}, journal = {{Chemical Engineering Science}}, keywords = {{Sandwich packings Structured packings Rate-based approach Model validation Ultra-fast X-ray tomography}}, publisher = {{Elsevier BV}}, title = {{{A rate-based model for reactive separation columns with sandwich packings}}}, doi = {{10.1016/j.ces.2025.122681}}, volume = {{321}}, year = {{2025}}, } @inproceedings{63019, author = {{Donner, Johannes Aurelius Tamino and Schlüter, Alexander}}, booktitle = {{SDEWES Conference 2025}}, keywords = {{5GDHC, district heating, DHC, waste heat, AI-Driven}}, location = {{Dubrovnik}}, title = {{{Development of an AI-driven decentralized control for fifth generation district heating and cooling networks}}}, year = {{2025}}, } @inproceedings{62149, abstract = {{The increasing complexity of technical systems requires early, structured verification and validation (V&V). Existing metadata models only map parts of the engineering process and do not enable a continuous chain of effects from requirements to test results. The aim of this publication is to develop a holistic V&V metadata model for the consistent, transparent and machine-processable description and linking of relevant engineering artifacts. In a five-stage research approach, essential model components are derived from literature and are integrated into a holistic model. Initial applications as part of a European research project show the potential of the model for a well-founded effect chain analysis and decision-supporting V&V processes.}}, author = {{Gräßler, Iris and Ebel, Marcel}}, booktitle = {{DS 140: Proceedings of the 36th Symposium Design for X (DFX2025)}}, keywords = {{verification, metadata model, Systems Engineering}}, location = {{Hamburg}}, publisher = {{The Design Society}}, title = {{{Ganzheitliches Metadatenmodel für die Verifikation und Validierung in der Entwicklung komplexer technischer Systeme}}}, doi = {{10.35199/dfx2025.09}}, year = {{2025}}, } @unpublished{53793, abstract = {{We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.}}, author = {{Harder, Hans and Peitz, Sebastian}}, keywords = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}}, title = {{{Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}}}, year = {{2024}}, } @inproceedings{54807, abstract = {{This paper considers the shape formation problem within the 3D hybrid model, where a single agent with a strictly limited viewing range and the computational capacity of a deterministic finite automaton manipulates passive tiles through pick-up, movement, and placement actions. The goal is to reconfigure a set of tiles into a specific shape termed an icicle. The icicle, identified as a dense, hole-free structure, is strategically chosen to function as an intermediate shape for more intricate shape formation tasks. It is designed for easy exploration by a finite state agent, enabling the identification of tiles that can be lifted without breaking connectivity. Compared to the line shape, the icicle presents distinct advantages, including a reduced diameter and the presence of multiple removable tiles. We propose an algorithm that transforms an arbitrary initially connected tile structure into an icicle in 𝒪(n³) steps, matching the runtime of the line formation algorithm from prior work. Our theoretical contribution is accompanied by an extensive experimental analysis, indicating that our algorithm decreases the diameter of tile structures on average.}}, author = {{Hinnenthal, Kristian and Liedtke, David Jan and Scheideler, Christian}}, booktitle = {{3rd Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2024)}}, editor = {{Casteigts, Arnaud and Kuhn, Fabian}}, isbn = {{978-3-95977-315-7}}, issn = {{1868-8969}}, keywords = {{Programmable Matter, Shape Formation, 3D Model, Finite Automaton}}, pages = {{15:1–15:20}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures}}}, doi = {{10.4230/LIPIcs.SAND.2024.15}}, volume = {{292}}, year = {{2024}}, } @inproceedings{53811, abstract = {{Persistent security challenges plague DevOps teams due to a deficiency in expertise regarding security tools and methods, as evidenced by frequent security incidents. Existing maturity models fail to adequately address the specific needs of DevOps teams. In response, this paper proposes "Security Belts," a novel maturity model inspired by martial arts ranking systems. This model aims to assist DevOps teams in enhancing their security capabilities by providing a structured approach, starting with fundamental activities and progressing to more advanced techniques. Drawing from the experiences of monitoring 21 teams, the paper presents lessons learned and offers actionable advice for refining maturity models tailored to software quality improvement.}}, author = {{Taaibi, Samira and Dziwok, Stefan and Hermerschmidt, Lars and Koch, Thorsten and Merschjohann, Sven and Vollmary, Mark}}, keywords = {{Software security, maturity model}}, location = {{Salt Lake City}}, title = {{{Security Belts: A Maturity Model for DevOps Teams to Increase the Software Security of their Product - An Experience Report}}}, year = {{2024}}, } @inproceedings{56166, abstract = {{Developing Intelligent Technical Systems (ITS) involves a complex process encompassing planning, analysis, design, production, and maintenance. Model-Based Systems Engineering (MBSE) is a key methodology for systematic systems engineering. Designing models for ITS requires harmonious interaction of various elements, posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we generated a dataset for modeling, using prompt engineering on large language models. The generated artifacts can aid engineers in MBSE design or serve as synthetic training data for AI assistants.}}, author = {{Kulkarni, Pranav Jayant and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}}, booktitle = {{DS 130: Proceedings of NordDesign 2024}}, editor = {{Malmqvist, J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}}, keywords = {{Data Driven Design, Design Automation, Systems Engineering (SE), Artificial Intelligence (AI)}}, location = {{Reykjavik}}, pages = {{617--625}}, title = {{{Towards Automated Design: Automatically Generating Modeling Elements with Prompt Engineering and Generative Artificial Intelligence}}}, doi = {{10.35199/NORDDESIGN2024.66}}, year = {{2024}}, } @phdthesis{54552, abstract = {{Die vorliegende Dissertation beschreibt das Konzept und den Entwicklungsprozess eines Lichtsignalanlagenregelungssystems bis zur Realumsetzung. Das Regelungssystem, welches im Rahmen des Pilotprojekts Schlosskreuzung entstanden ist, besteht dabei aus zwei untereinander abgestimmten Methoden zur Echtzeit-Verkehrsrekonstruktion und zur modellprädiktiven Regelung des Verkehrssystems anhand der vorhandenen Lichtsignalanlagen. Die Echtzeit-Verkehrsrekonstruktion approximiert dabei simulationsbasiert den aktuellen Verkehrszustand anhand gegebener Messdaten über dynamische Verkehrszuweisungen. Die entwickelte mehrstufige Lichtsignalanlagenregelung nutzt ein Fuzzy-System zur Phasenvorauswahl, um anschließend über eine modellprädiktive Regelung das nichtlineare Problem mit dem Ergebnis der optimalen Kombinationen von Phasen und Schaltzeitpunkten zu lösen. Das Regelungssystem wird in dieser Arbeit anhand eines ausgewählten Verkehrsgebiets zunächst rein simulativ getestet und mit dem vorliegenden Bestandsverfahren verglichen. Im Anschluss an die prototypische Inbetriebnahme wird dieser Vergleich durch die Feldtests abgeschlossen. Entsprechende Ergebnisse zeigen das große Potential der Entwicklung hinsichtlich der Reduktion von Kriterien wie Emissionen oder Wartezeiten und gleichzeitig den Handlungsbedarf für eine standardmäßige Nutzung.}}, author = {{Malena, Kevin}}, isbn = {{978-3-947647-41-5}}, keywords = {{Traffic Light System Control, Model Predictive Control}}, pages = {{207}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Konzipierung, Analyse und Realumsetzung eines mehrstufigen modellprädiktiven Lichtsignalanlagenregelungssystems}}}, doi = {{10.17619/UNIPB/1-2021}}, volume = {{422}}, year = {{2024}}, } @inproceedings{56660, abstract = {{In a successful dialogue in general and a successful explanation in specific, partners need to account for both, the task model (what is relevant for the task) and the partner model (what one can con- tribute). The phenomenon of coupling between task and the partner model becomes especially interesting in the context of Human– Robot Interaction where humans have to deal with unknown ca- pabilities of the robot, which can momentarily be perceived when the robot is unable to contribute to the task. Following research on the path over manner prominence in an action [31–33], a robot ex- plained actions to a human by emphasizing two aspects – the path ("where" component) and the manner ("how" component). On criti- cal trials, the robot occasionally omitted one of these components where participants sought missing information for the path or the manner. Participants’ information-seeking and gaze behaviour were analysed. Analysis confirms the initial predictions for, a) task model (path over manner prominence), i.e., earlier information-seeking for path-missing than manner-missing trials, and b) partner model, i.e., while information-seeking is predominantly tied to the attention on the robot’s face, when robot fails to provide resolution, attention shifts more often towards its torso – a behavior likely to indicate an exploration of the robot’s capabilities. An individual-level anal- ysis further confirms that the intra-individual variation in the task model is partly influenced by the perceived capability of the robot.}}, author = {{Singh, Amit and Rohlfing, Katharina J.}}, booktitle = {{Proceedings of 26th ACM International Conference on Multimodal Interaction (ICMI 2024)}}, keywords = {{Explanation, Scaffolding, Eyetracking, Partner Model, HRI}}, location = {{San Jose, Costa Rica}}, title = {{{Coupling of Task and Partner Model: Investigating the Intra-Individual Variability in Gaze during Human–Robot Explanatory Dialogue}}}, doi = {{10.1145/3686215.3689202}}, year = {{2024}}, } @inproceedings{56918, abstract = {{Joint value creation of organizations in ecosystems have a high failure rate, stressing the need for tools that enable the alignment of business models through visual inquiry. However, existing visual inquiry tools rarely consider recent design knowledge or ecosystem understanding. This leads to dissatisfied users and impedes the full realization of ecosystems’ potential. This short paper proposes an archaeological design science approach for enhancing the design of visual inquiry tools (e.g., a canvas) for ecosystems. Preliminary findings reveal 24 relevant artifacts, and shortcomings in the creation of conceptual models and rigorous evaluations. The proposed research process aims to develop design principles for more effective tools to bridge the gap between visual inquiry tools and ecosystems. This research contributes to design science research by reutilizing design knowledge and further developing the archaeological design approach. It also offers valuable information to practitioners about existing business model tools for the creation of ecosystems.}}, author = {{Vorbohle, Christian}}, booktitle = {{Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024)}}, keywords = {{Design Science Research, Design Archaeology, Canvas Analysis, Business Model Tools}}, location = {{Paphos, Cyprus}}, title = {{{Bridging Boundaries: Enhancing Visual Inquiry Tools for Ecosystems through Design Archaeology}}}, year = {{2024}}, } @inproceedings{57085, abstract = {{We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for diarization in a joint statistical framework. Through the integration, both spatial and spectral information are exploited for diarization and separation. We also develop a method for counting the number of active speakers in a segment of a meeting to support block-wise processing. While the total number of speakers in a meeting may be known, it is usually not known on a per-segment level. With the proposed speaker counting, joint diarization and source separation can be done segment-by-segment, and the permutation problem across segments is solved, thus allowing for block-online processing in the future. Experimental results on the LibriCSS meeting corpus show that the integrated approach outperforms a cascaded approach of diarization and speech enhancement in terms of WER, both on a per-segment and on a per-meeting level.}}, author = {{Cord-Landwehr, Tobias and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, keywords = {{diarization, source separation, mixture model, meeting}}, location = {{Hyderabad, India}}, title = {{{Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models}}}, doi = {{10.1109/ICASSP49660.2025.10888445}}, year = {{2024}}, } @inproceedings{52369, abstract = {{Megatrends, such as digitization or sustainability, are confronting the product management of manufacturing companies with a variety of challenges regarding the design of future products, but also the management of the actual products. To successfully position their products in the market, product managers need to gather and analyze comprehensive information about customers, developments in the products’ environment, product usage, and more. The digitization of all aspects of life is making data on these topics increasingly available – via social media, documents, or the internet of things from the products themselves. The systematic collection and analysis of these data enable the exploitation of new potentials for the adaption of existing products and the creation of the products of tomorrow. However, there are still no insights into the main concepts and cause-effect relationships in exploiting data-driven approaches for product management. Therefore, this paper aims to identify the main concepts and advantages of data-driven product management. To answer the corresponding research questions a comprehensive systematic literature review is conducted. From its results, a detailed description of the main concepts of data-driven product management is derived. Furthermore, a taxonomy for the advantages of data-driven product management is presented. The main concepts and the taxonomy allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.}}, author = {{Fichtler, Timm and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}}, keywords = {{Product Lifecyle Management (PLM), Data Analytics, Data-driven Design, Engineering Management, Lifecycle Data}}, location = {{Rabat, Morocco}}, publisher = {{IEEE}}, title = {{{Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research}}}, doi = {{10.1109/ictmod59086.2023.10438135}}, year = {{2023}}, } @article{53801, abstract = {{In this study, we evaluate the impact of gender-biased data from German-language physician reviews on the fairness of fine-tuned language models. For two different downstream tasks, we use data reported to be gender biased and aggregate it with annotations. First, we propose a new approach to aspect-based sentiment analysis that allows identifying, extracting, and classifying implicit and explicit aspect phrases and their polarity within a single model. The second task we present is grade prediction, where we predict the overall grade of a review on the basis of the review text. For both tasks, we train numerous transformer models and evaluate their performance. The aggregation of sensitive attributes, such as a physician’s gender and migration background, with individual text reviews allows us to measure the performance of the models with respect to these sensitive groups. These group-wise performance measures act as extrinsic bias measures for our downstream tasks. In addition, we translate several gender-specific templates of the intrinsic bias metrics into the German language and evaluate our fine-tuned models. Based on this set of tasks, fine-tuned models, and intrinsic and extrinsic bias measures, we perform correlation analyses between intrinsic and extrinsic bias measures. In terms of sensitive groups and effect sizes, our bias measure results show different directions. Furthermore, correlations between measures of intrinsic and extrinsic bias can be observed in different directions. This leads us to conclude that gender-biased data does not inherently lead to biased models. Other variables, such as template dependency for intrinsic measures and label distribution in the data, must be taken into account as they strongly influence the metric results. Therefore, we suggest that metrics and templates should be chosen according to the given task and the biases to be assessed. }}, author = {{Kersting, Joschka and Maoro, Falk and Geierhos, Michaela}}, issn = {{0169-023X}}, journal = {{Data & Knowledge Engineering}}, keywords = {{Language model fairness, Aspect phrase classification, Grade prediction, Physician reviews}}, publisher = {{Elsevier}}, title = {{{Towards comparable ratings: Exploring bias in German physician reviews}}}, doi = {{10.1016/j.datak.2023.102235}}, volume = {{148}}, year = {{2023}}, } @phdthesis{41971, abstract = {{Ultraschall-Drahtbonden ist eine Standardtechnologie im Bereich der Aufbau- und Verbindungstechnik von Leistungshalbleitermodulen. Um Prozessschritte und damit wertvolle Zeit zu sparen, sollen die Kupferdickdrähte für die Leistungshalbleiter auch für die Kontaktierung von eingespritzten Anschlusssteckern im Modulrahmen verwendet werden. Das Kontaktierungsverfahren mit diesen Drähten auf Steckern in dünnwandigen Kunststoffrahmen führt häufig zu unzureichender Bondqualität. In dieser Arbeit wird das Bonden von Anschlusssteckern experimentell und anhand von Simulationen untersucht, um die Prozessstabilität zu steigern. Zunächst wurden Experimente auf Untergründen mit hoher Steifigkeit durchgeführt, um Störgrößen von Untergrundeigenschaften zu verringern. Die gewonnenen Erkenntnisse erlaubten die Entwicklung eines Simulationsmodells für die Vorhersage der Bondqualität. Dieses basiert auf einer flächenaufgelösten Reibarbeitsbestimmung im Fügebereich unter Berücksichtigung des Ultraschallerweichungseffektes und der hierdurch entstehenden hohen Drahtverformung. Experimente an den Anschlusssteckern im Modulrahmen zeigten eine verringerte Relativverschiebung zwischen Draht und Stecker, was zu einer deutlichen Verringerung der Reibarbeit führt. Außerdem wurden verminderte Schwingamplituden des Bondwerkzeugs nachgewiesen. Dies führt zu einer weiteren Reduktion der Reibarbeit. Beide Effekte wurden mithilfe eines Mehrmassenschwingers modelliert. Die gewonnenen Erkenntnisse und die erstellten Simulationsmodelle ermöglichen die Entwicklung von Klemmvorrichtungen, welche die identifizierten Störgrößen gezielt kompensieren und so ein verlässliches Bonden der Anschlussstecker im gleichen Prozessschritt ermöglichen, in dem auch die Leistungshalbleiter kontaktiert werden.}}, author = {{Althoff, Simon}}, isbn = {{978-3-8440-8903-5}}, keywords = {{heavy copper bonding, wire bonding, quality prediction, friction model, point-contact-element}}, pages = {{192}}, publisher = {{Shaker}}, title = {{{Predicting the Bond Quality of Heavy Copper Wire Bonds using a Friction Model Approach}}}, volume = {{15}}, year = {{2023}}, } @article{44672, abstract = {{With enhancing digitalization, condition monitoring is used in an increasing number of application fields across various industrial sectors. By its application, increased reliability as well as reduced risks and costs can be achieved. Based on different approaches, technical systems are monitored and measured data is analyzed to enable condition-based or predictive maintenance. To this end, machine learning approaches are usually implemented to diagnose the health states or predict the health index of the monitored system. However, these trained models are often black-box models, not intuitively explainable for a human. To overcome this shortcoming, a model-based approach based on physics is developed for piezoelectric bending actuators. Such a model enables a transparent representation of the system. Moreover, the model-based approach is extended by a parameter-estimation to account for sudden changes in behavior e. g. caused by occurring cracks.}}, author = {{Bender, Amelie}}, issn = {{0924-4247}}, journal = {{Sensors and Actuators A: Physical}}, keywords = {{Condition Monitoring, Model-based approach Diagnostics, Varying conditions, Explainability, Piezoelectric bending actuators}}, publisher = {{Elsevier BV}}, title = {{{Model-based condition monitoring of piezoelectric bending actuators}}}, doi = {{10.1016/j.sna.2023.114399}}, volume = {{357}}, year = {{2023}}, } @inproceedings{45793, abstract = {{The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.}}, author = {{Grigoryan, Khoren and Fichtler, Timm and Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu, Roman and Koldewey, Christian}}, booktitle = {{Procedia CIRP 33}}, keywords = {{Product Management, Data Analytics, Data-Driven Design, Product-related data, Lifecycle Data, Tool-support}}, location = {{Sydney}}, title = {{{Data-Driven Product Management: A Practitioner-Driven Research Agenda}}}, year = {{2023}}, }