TY - CHAP A1 - Berger, Ulrich A1 - Kretzschmann, Ralf A1 - Reichenbach, Matthias A1 - Zürn, Michael A1 - Vargas, Veronica T1 - Development of a heuristic Process Planning Tool for Sequencing NC Machining Operations extended by the Potential Field Analysis T2 - 13th IFAC Symposium on Information Control Problems in Manufacturing 2009, Moscow, Russia, 3 - 5 June 2009 Y1 - 2009 SN - 978-1-617-38021-1 PB - Curran CY - Red Hook, NY ER - TY - JOUR A1 - Berger, Ulrich A1 - Reichenbach, Matthias A1 - Zürn, Michael A1 - Klumpp, Willi T1 - Innovative Konzeptentwicklung für Robotermontagen mit Axiomatic System Design und Potentialfeldanalyse Y1 - 2008 ER - TY - CHAP A1 - Reichenbach, Matthias A1 - Zürn, Michael A1 - Klumpp, Willi A1 - Berger, Ulrich ED - Müller, Egon T1 - Innovative Montagekonzepte mit Axiomatic System Design und Potentialfeldanalyse, aus Wissenschaft und Praxis T2 - Wandlungsfähige Produktionssysteme, Tagungsband, TBI '08, 13. Tage des Betriebs- und Systemingenieurs, 2. Symposium Wissenschaft und Praxis, Chemnitz, 13. November 2008 Y1 - 2008 SP - 335 EP - 344 PB - Inst. für Betriebswiss. und Fabriksysteme CY - Chemnitz ER - TY - CHAP A1 - Andulkar, Mayur A1 - Hodapp, Jan A1 - Reichling, Thorsten A1 - Reichenbach, Matthias A1 - Berger, Ulrich T1 - Training CNNs from Synthetic Data for Part Handling in Industrial Environments T2 - IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018 N2 - 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. KW - CAD KW - cellular neural nets KW - convolution KW - data handling KW - feedforward neural nets KW - image processing KW - industrial engineering KW - learning (artificial intelligence) KW - materials handling KW - production engineering computing KW - rendering (computer graphics) Y1 - 2018 SN - 978-1-5386-3593-3 U6 - https://doi.org/10.1109/COASE.2018.8560470 SP - 624 EP - 629 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Reichenbach, Matthias T1 - Montage 21 - neue Wege zu innovativen Montagekonzepten am Beispiel der Hinterachsmontage T2 - Planung effizienter und attraktiver Fabriken, Frauenhofer Institut Stuttgart (IPA) Y1 - 2008 PB - m-i-c CY - Landsberg ER - TY - CHAP A1 - Reichenbach, Matthias A1 - Berger, Ulrich T1 - Umgebung einer Planungsumgebung für Montageaufgaben, dargestellt am Beispiel der Hinterachsgetriebemontage mit dem Leichtbauroboter T2 - Entwurf komplexer Automatisierungssysteme, EKA 2010, 11. Fachtagung mit Tutorium, 25. bis 27. Mai 2010 in Magdeburg, Denkfabrik im Wissenschaftshafen Y1 - 2010 SN - 978-3-940961-41-9 SP - 109 EP - 114 PB - ifak, Institut für Automation und Kommunikation e.V CY - Magdeburg ER - TY - GEN A1 - Schiemann, Markus A1 - Berger, Ulrich A1 - Zürn, Michael A1 - Reichenbach, Matthias T1 - Mensch-Roboter-Kollaboration im Spannungsfeld von Rekonfigurierbarkeit, Sicherheit und Effizienz T2 - 20. Leitkongress der Mess- und Automatisierungstechnik ; AUTOMATION 2019 KW - Mensch-Roboter-Kollaboration KW - Sicherheitstechnik Y1 - 2019 SN - 978-3-18-092351-2 SP - 5 EP - 14 PB - VDI CY - Baden-Banden ER - TY - GEN A1 - Reichling, Thorsten A1 - Hodapp, Jan A1 - Berger, Ulrich A1 - Zürn, Michael A1 - Reichenbach, Matthias T1 - Smarte Produktionsassistenten für die wandlungsfähige Produktion (smart manufacturing assistants for the versatile production) T2 - atp edition N2 - 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. KW - Wandlungsfähigkeit KW - Mensch-Roboter-Kooperation KW - Asset-to-Go KW - Machine Learning Y1 - 2017 SN - 2364-3137 SN - 0178-2320 VL - 59 IS - 5 SP - 44 EP - 51 ER - TY - CHAP A1 - Sarivan, Ioan-Matei A1 - Baumann, Stefan Andreas A1 - Díez Álvarez, Daniel A1 - Euteneuer, Felix A1 - Reichenbach, Matthias A1 - Berger, Ulrich A1 - Madsen, Ole A1 - Bøgh, Simon ED - Weißgraeber, Philipp ED - Heieck, Frieder ED - Ackermann, Clemens T1 - Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification T2 - Advances in Automotive Production Technology – Theory and Application; Stuttgart Conference on Automotive Production (SCAP2020) N2 - 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. Y1 - 2021 SN - 978-3-662-62961-1 U6 - https://doi.org/10.1007/978-3-662-62962-8_26 SP - 221 EP - 229 PB - Springer CY - Berlin ER - TY - GEN A1 - Hodapp, Jan A1 - Schiemann, Markus A1 - Bilous, Vadym A1 - Arcidiacono, Claudio Salvatore A1 - Reichenbach, Matthias T1 - Advances in Automated Generation of Convolutional Neural Networks from Synthetic Data in Industrial Environment T2 - 53rd Hawaii International Conference on System Sciences: Maui, Hawaii, United States of America, Jan 07 - 10, 2020 N2 - 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. Y1 - 2020 UR - https://hdl.handle.net/10125/64307 SN - 978-0-9981331-3-3 U6 - https://doi.org/10.24251/HICSS.2020.565 ER - TY - THES A1 - Reichenbach, Matthias T1 - Entwicklung einer Planungsumgebung für Montageaufgaben in der wandlungsfähigen Fabrik, dargestellt am Beispiel des impedanzgeregelten Leichtbauroboters Y1 - 2010 SN - 978-3-8322-9379-6 PB - Shaker CY - Aachen ER - TY - GEN A1 - Shahin, Keyvan A1 - Rotta, Randolf A1 - Archila, Oscar A1 - Mykytyn, Pavlo A1 - Nattke, Matthias A1 - Reichenbach, Marc A1 - Nolte, Jörg A1 - Natarov, Roman T1 - A Modular Communication Architecture for Adaptive UAV Swarms T2 - 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS) N2 - 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. Y1 - 2023 SN - 979-8-3503-4647-3 SN - 979-8-3503-4648-0 U6 - https://doi.org/10.1109/COINS57856.2023.10189245 PB - IEEE CY - Berlin ER - TY - GEN A1 - Pechmann, Stefan A1 - Mai, Timo A1 - Völkel, Matthias A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez, Eduardo A1 - Perez-Bosch Quesada, Emilio A1 - Reichenbach, Marc A1 - Wenger, Christian A1 - Hagelauer, Amelie T1 - A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells T2 - Electronics N2 - 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. KW - RRAM KW - Multilevel switching KW - Programming circuit Y1 - 2021 U6 - https://doi.org/10.3390/electronics10050530 SN - 2079-9292 VL - 10 IS - 5 ER -