@inproceedings{58419, author = {{Von Heissen, Oliver and Hanke, Fabian and Mpidi Bita, Isaac and Hovemann, Aschot and Dumitrescu, Roman}}, booktitle = {{Proceedings of NordDesign 2024}}, publisher = {{The Design Society}}, title = {{{Toward Intelligent Generation of System Architectures}}}, doi = {{10.35199/norddesign2024.54}}, year = {{2024}}, } @article{58425, abstract = {{Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation.}}, author = {{Weller, Julian and Migenda, Nico and Naik, Yash and Heuwinkel, Tim and Kühn, Arno and Kohlhase, Martin and Schenck, Wolfram and Dumitrescu, Roman}}, issn = {{2227-7390}}, journal = {{Mathematics}}, number = {{17}}, publisher = {{MDPI AG}}, title = {{{Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories}}}, doi = {{10.3390/math12172663}}, volume = {{12}}, year = {{2024}}, } @article{58421, author = {{von Enzberg, Sebastian and Weller, Julian and Brock, Jonathan and Merkelbach, Silke and Panzner, Melina and Lick, Jonas and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, pages = {{1454--1461}}, publisher = {{Elsevier BV}}, title = {{{On the Current State of Industrial Data Science: Challenges, Best Practices, and Future Directions}}}, doi = {{10.1016/j.procir.2024.10.266}}, volume = {{130}}, year = {{2024}}, } @article{58422, author = {{Weller, Julian and Migenda, Nico and Enzberg, Sebastian von and Kohlhase, Martin and Schenck, Wolfram and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, pages = {{424--429}}, publisher = {{Elsevier BV}}, title = {{{Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories}}}, doi = {{10.1016/j.procir.2024.03.022}}, volume = {{128}}, year = {{2024}}, } @article{58420, author = {{Hartmann, Stefan and Brock, Jonathan and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, pages = {{100--105}}, publisher = {{Elsevier BV}}, title = {{{Applying Artificial Intelligence in the Smart Factory: Lessons Learned from real-world use cases}}}, doi = {{10.1016/j.procir.2024.10.062}}, volume = {{130}}, year = {{2024}}, } @inproceedings{58423, author = {{Weller, Julian and Migenda, Nico and Kühn, Arno and Dumitrescu, Roman}}, location = {{Hawaii, Honululu}}, publisher = {{LibreCat University}}, title = {{{Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories}}}, doi = {{10.15488/17721}}, year = {{2024}}, } @inproceedings{58431, author = {{Merkelbach , Silke and Diedrich , Alexander and Sztyber-Betley, Anna and Travé-Massuyès, Louise and Chanthery , Elodie and Niggemann , Oliver and Dumitrescu , Roman }}, booktitle = {{Open Access Series in Informatics (OASIcs)}}, location = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)}}, pages = {{31:1--31:15}}, title = {{{Using Multi-Modal LLMs to Create Models for Fault Diagnosis}}}, volume = {{125}}, year = {{2024}}, } @inproceedings{58424, author = {{Niederhaus, Marvin and Migenda, Nico and Weller, Julian and Schenck, Wolfram and Kohlhase, Martin}}, booktitle = {{2024 35th Conference of Open Innovations Association (FRUCT)}}, publisher = {{IEEE}}, title = {{{Technical Readiness of Prescriptive Analytics Platforms: A Survey}}}, doi = {{10.23919/fruct61870.2024.10516367}}, year = {{2024}}, } @article{58435, author = {{Wilke, Daria and Mansheim, Johanna and Dumitrescu, Roman}}, issn = {{0360-8581}}, journal = {{IEEE Engineering Management Review}}, pages = {{1--14}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Challenges and Research Needs in the Offer Phase of Special Purpose Machinery and Plant Engineering – A Qualitative Study with Industry Experts}}}, doi = {{10.1109/emr.2024.3503994}}, year = {{2024}}, } @inproceedings{58432, author = {{Merkelbach, Silke and Heuwinkel, Tim and Dumitrescu, Roman}}, booktitle = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, publisher = {{IEEE}}, title = {{{PID-Gen: Towards an Algorithm for the Generation of Random P&IDs}}}, doi = {{10.1109/etfa61755.2024.10710643}}, year = {{2024}}, } @inproceedings{58436, author = {{Humpert, Lynn and Wilke, Daria and Tissen, Denis and Rummney, Johannes and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{2024 IEEE International Systems Conference (SysCon)}}, publisher = {{IEEE}}, title = {{{Investigation of validation methods for system design in the B2B sector}}}, doi = {{10.1109/syscon61195.2024.10553571}}, year = {{2024}}, } @inproceedings{58434, author = {{Wilke, Daria and Humpert, Lynn and Suwal, Pragita and Dumitrescu, Roman}}, booktitle = {{Proceedings of NordDesign 2024}}, publisher = {{The Design Society}}, title = {{{Framework for the Selection of Systems Engineering Modelling Languages and Methods for Special Purpose Machinery}}}, doi = {{10.35199/norddesign2024.59}}, year = {{2024}}, } @inproceedings{58433, author = {{Hanke, Fabian and Moallim, Karan and Bernijazov, Ruslan and Demir, Riza and Brunnhausser, Jorg and Dumitrescu, Roman and Lindow, Kai}}, booktitle = {{Proceedings of NordDesign 2024}}, publisher = {{The Design Society}}, title = {{{Intelligent Part Comparison in Computer Aided Design}}}, doi = {{10.35199/norddesign2024.16}}, 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}}, } @inbook{58440, author = {{Wippermann, Jan and Meschut, Gerson}}, booktitle = {{Proceedings of the Munich Symposium on Lightweight Design 2023}}, isbn = {{9783031646683}}, publisher = {{Springer Nature Switzerland}}, title = {{{Effect of Water Absorption on the Clamp Load of Plastic Nuts}}}, doi = {{10.1007/978-3-031-64669-0_5}}, year = {{2024}}, } @inproceedings{58445, author = {{Wyrwich, Fabian and Tschirner, Christian and Bohnenkamp, Tinus and Hovemann, Aschot and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2024}}, location = {{Leipzig}}, title = {{{Anwendung und Integration des Application Lifecycle Managements im Produktlebenszyklus-Ergebnisse einer Industrie-Studie}}}, year = {{2024}}, } @inproceedings{56168, author = {{Tissen, Denis and Wiederkehr, Ingrid and Bernijazov, Ruslan and Koldewey, Christian and Dumitrescu, Roman}}, location = {{Kyoto, japan}}, title = {{{Portals across domains: A data analysis task and documentation canvas for data-driven model-based systems engineering}}}, year = {{2024}}, } @inproceedings{58443, author = {{Wilke, Daria and Grote, Eva-Maria and Schierbaum, Anja and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2024}}, location = {{Leipzig}}, title = {{{Messbare Erfolgskriterien von Systems Engineering}}}, year = {{2024}}, } @article{58442, author = {{Wippermann, Jan and Meschut, Gerson}}, journal = {{JOINING PLASTICS}}, title = {{{Analyse des Ein- und Überdrehmoments von Kunststoffmuttern in Abhängigkeit von der Luftfeuchtigkeit}}}, volume = {{18}}, 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}}, }