Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-7609 Konferenzveröffentlichung Berger, Ulrich; Kretzschmann, Ralf; Reichenbach, Matthias; Zürn, Michael; Vargas, Veronica Development of a heuristic Process Planning Tool for Sequencing NC Machining Operations extended by the Potential Field Analysis Red Hook, NY Curran 13th IFAC Symposium on Information Control Problems in Manufacturing 2009, Moscow, Russia, 3 - 5 June 2009 978-1-617-38021-1 FG Automatisierungstechnik OPUS4-7610 Wissenschaftlicher Artikel Berger, Ulrich; Reichenbach, Matthias; Zürn, Michael; Klumpp, Willi Innovative Konzeptentwicklung für Robotermontagen mit Axiomatic System Design und Potentialfeldanalyse FG Automatisierungstechnik OPUS4-7611 Konferenzveröffentlichung Reichenbach, Matthias; Zürn, Michael; Klumpp, Willi; Berger, Ulrich Müller, Egon Innovative Montagekonzepte mit Axiomatic System Design und Potentialfeldanalyse, aus Wissenschaft und Praxis Chemnitz Inst. für Betriebswiss. und Fabriksysteme 9 Wandlungsfähige Produktionssysteme, Tagungsband, TBI '08, 13. Tage des Betriebs- und Systemingenieurs, 2. Symposium Wissenschaft und Praxis, Chemnitz, 13. November 2008 335 344 FG Automatisierungstechnik OPUS4-23126 Konferenzveröffentlichung Andulkar, Mayur; Hodapp, Jan; Reichling, Thorsten; Reichenbach, Matthias; Berger, Ulrich Training CNNs from Synthetic Data for Part Handling in Industrial Environments As Convolutional Neural Network based models become reliable and efficient, two questions arise in relation to their applications for industrial purposes. The usefulness of these models in industrial environments and their implementation in these settings. This paper describes the autonomous generation of Region based CNN models trained on images from rendered CAD models and examines their applicability and performance for part handling application. The development of the automated synthetic data generation is detailed and two CNN models are trained with the aim to detect a car component and differentiate it against another similar looking part. The performance of these models is tested on real images and it was found that the proposed approach can be easily adopted for detecting a range of parts in arbitrary backgrounds. Moreover, the use of syntheic images for training CNNs automates the process of generating a detector. Piscataway, NJ IEEE 5 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018 978-1-5386-3593-3 624 629 10.1109/COASE.2018.8560470 FG Automatisierungstechnik OPUS4-31239 misc Shahin, Keyvan; Rotta, Randolf; Archila, Oscar; Mykytyn, Pavlo; Nattke, Matthias; Reichenbach, Marc; Nolte, Jörg; Natarov, Roman A Modular Communication Architecture for Adaptive UAV Swarms Small Unmanned Aerial Vehicles (UAVs) have vast application potential from industrial logistics and disaster monitoring to smart farming, for example, to create maps with Normalized Difference Vegetation Index (NDVI) cameras and AI-based image classification or the precision spot application of fertilizers. Operating multiple UAVs in parallel, including those with different specializations, enables efficient coverage of large areas. While existing research focused mainly on predefined flight formations for swarms or autonomous missions for single UAVs, the focus of this work is on multiple UAVs with individually adaptable missions. We derive communication system requirements from swarm coordination algorithms and present a respective design. The resulting modular architecture enables future research on autonomous adaptive UAV swarms and their use as flying sensor platforms. Berlin IEEE 6 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS) 979-8-3503-4647-3 10.1109/COINS57856.2023.10189245 FG Technische Informatik OPUS4-16933 Konferenzveröffentlichung Reichenbach, Matthias Montage 21 - neue Wege zu innovativen Montagekonzepten am Beispiel der Hinterachsmontage Landsberg m-i-c Planung effizienter und attraktiver Fabriken, Frauenhofer Institut Stuttgart (IPA) FG Automatisierungstechnik OPUS4-16939 Konferenzveröffentlichung Reichenbach, Matthias; Berger, Ulrich Umgebung einer Planungsumgebung für Montageaufgaben, dargestellt am Beispiel der Hinterachsgetriebemontage mit dem Leichtbauroboter Magdeburg ifak, Institut für Automation und Kommunikation e.V 5 Entwurf komplexer Automatisierungssysteme, EKA 2010, 11. Fachtagung mit Tutorium, 25. bis 27. Mai 2010 in Magdeburg, Denkfabrik im Wissenschaftshafen 978-3-940961-41-9 109 114 FG Automatisierungstechnik OPUS4-24204 misc Schiemann, Markus; Berger, Ulrich; Zürn, Michael; Reichenbach, Matthias Mensch-Roboter-Kollaboration im Spannungsfeld von Rekonfigurierbarkeit, Sicherheit und Effizienz Baden-Banden VDI 9 20. Leitkongress der Mess- und Automatisierungstechnik ; AUTOMATION 2019 978-3-18-092351-2 5 14 FG Automatisierungstechnik OPUS4-20469 misc Reichling, Thorsten; Hodapp, Jan; Berger, Ulrich; Zürn, Michael; Reichenbach, Matthias Smarte Produktionsassistenten für die wandlungsfähige Produktion (smart manufacturing assistants for the versatile production) Die heutige Automobilindustrie ist durch zunehmende Individualisierungswünsche der Kunden sowie einer Verkürzung der Konjunkturzyklen gekennzeichnet. Für Fahrzeughersteller, wie der Daimler AG äußert sich diese Marktsituation in einer hohen Variantenvielfalt und Stückzahlschwankungen. Da die Ausprägungen dieser Einflüsse weitgehend unbekannt sind, muss das Produktionssystem darauf vorbereitet sein, bedarfsgerecht der Marktsituation angepasst zu werden. Diese Anpassungen müssen schnell, mit wenig Aufwand und geringen Kosten möglich sein. Wandlungsfähige Produktionssysteme gehören deshalb zu den strategischen Erfolgsgrößen für die Automobilindustrie. Innerhalb dieses Beitrags wird ein modularer, skalierbarer Produktionsbaukasten vorgestellt, der Bestandteil einer Planungs- und Entwicklungsumgebung wandlungsfähiger roduktionskonzepte ist. Die hierfür genutzte grenzwerttheoretische Entwicklungsmethodik wird erläutert und die Skalierungsmöglichkeiten des Produktionsbaukastens mit Hilfe neuer Produktionstechnologien betrachtet. Dabei zeichnet sich die Mensch-Roboter-Kooperation als wesentlicher Bestandteil wandlungsfähiger Produktionskonzepte ab. „Smarte Produktionsassistenten" sind intelligente und sichere Roboter-Applikationen, die sich als Möglichkeit hervortun die Mensch-Roboter-Kooperation als Schlüsseltechnologie in industriellen Anwendungen zu realisieren. 7 atp edition 59 5 44 51 FG Automatisierungstechnik OPUS4-27064 misc Pechmann, Stefan; Mai, Timo; Völkel, Matthias; Mahadevaiah, Mamathamba Kalishettyhalli; Perez, Eduardo; Perez-Bosch Quesada, Emilio; Reichenbach, Marc; Wenger, Christian; Hagelauer, Amelie A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells In this work, we present an integrated read and programming circuit for Resistive Random Access Memory (RRAM) cells. Since there are a lot of different RRAM technologies in research and the process variations of this new memory technology often spread over a wide range of electrical properties, the proposed circuit focuses on versatility in order to be adaptable to different cell properties. The circuit is suitable for both read and programming operations based on voltage pulses of flexible length and height. The implemented read method is based on evaluating the voltage drop over a measurement resistor and can distinguish up to eight different states, which are coded in binary, thereby realizing a digitization of the analog memory value. The circuit was fabricated in the 130 nm CMOS process line of IHP. The simulations were done using a physics-based, multi-level RRAM model. The measurement results prove the functionality of the read circuit and the programming system and demonstrate that the read system can distinguish up to eight different states with an overall resistance ratio of 7.9. 17 Electronics 10 5 10.3390/electronics10050530 FG Halbleitermaterialien OPUS4-28625 Teil eines Buches Sarivan, Ioan-Matei; Baumann, Stefan Andreas; Díez Álvarez, Daniel; Euteneuer, Felix; Reichenbach, Matthias; Berger, Ulrich; Madsen, Ole; Bøgh, Simon Weißgraeber, Philipp; Heieck, Frieder; Ackermann, Clemens Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70%, thus furthering the necessary development needed to have a system ready to be used on a production environment. Berlin Springer 8 Advances in Automotive Production Technology – Theory and Application; Stuttgart Conference on Automotive Production (SCAP2020) 978-3-662-62961-1 221 229 10.1007/978-3-662-62962-8_26 FG Automatisierungstechnik OPUS4-25326 misc Hodapp, Jan; Schiemann, Markus; Bilous, Vadym; Arcidiacono, Claudio Salvatore; Reichenbach, Matthias Advances in Automated Generation of Convolutional Neural Networks from Synthetic Data in Industrial Environment The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best. 7 53rd Hawaii International Conference on System Sciences: Maui, Hawaii, United States of America, Jan 07 - 10, 2020 978-0-9981331-3-3 10.24251/HICSS.2020.565 FG Automatisierungstechnik OPUS4-7639 Dissertation Reichenbach, Matthias Entwicklung einer Planungsumgebung für Montageaufgaben in der wandlungsfähigen Fabrik, dargestellt am Beispiel des impedanzgeregelten Leichtbauroboters Aachen Shaker XV, 161 978-3-8322-9379-6 FG Automatisierungstechnik