TY - GEN A1 - Rehe, Grit A1 - Gebauer, Marc ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Stoettrup Schioenning Larsen, Maria ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - State of the Art of European Learning Factories for the Digital Transformation - A Survey on Technologies, Learning Concepts and Their Performance T2 - Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021 N2 - Learning factories constitute a promising approach for the acquisition of specific competencies, especially in terms of a digital transformation of the economy. Respectively, a variety of such factories differing in technology, learning concept, and potential audience have evolved. A precise and recent overview of those does not exist. However, such an overview is required for the implementation of concrete political measures, a future-oriented development of the individual learning factories, and an adequate selection by the audience. For this purpose, the authors investigate the current state of the art of European learning factories in the context of digitization. Thus, the terminology and definition of learning factories are provided. Moreover, using a structured literature review, the factories and their operation mode are outlined. Subsequently, the authors evaluate whether the different factories can build the required competencies among the audience and thus, support a successful digital transformation. Additionally, expert interviews with learning factory operators are performed to obtain profound information on the performance of learning factories. The findings help to assess the pertinency of European learning factories and provide a trace for their future development. Y1 - 2021 UR - https://link.springer.com/book/10.1007/978-3-030-90700-6 SN - 978-3-030-90699-3 SN - 978-3-030-90700-6 U6 - https://doi.org/10.1007/978-3-030-90700-6_102 SP - 895 EP - 902 PB - Springer CY - Cham ER - TY - GEN A1 - Berger, Ulrich A1 - Lehmann, Marlon Antonin A1 - Porsch, Ronny T1 - Selbstlernende Assistenzsysteme für Industrieroboter T2 - Industrie 4.0 Management N2 - Intelligente Assistenzsysteme unterstützen die Mitarbeiter in der Produktion und erhöhen die Effizienz durch das Einblenden von situationsbasierten Aufgabeninformationen. Im Projekt Advanced Robot Assistance Solution (ARAS) im Rahmen des Kuka Innovation Awards 2021 wurde eine Assistenztechnologie entwickelt, um automatisiert Roboterprogramme für Montageabläufe zu generieren. Durch innovative Mensch-Maschine-Schnittstellen werden Montageschritte per maschinellem Lernen erkannt und in ausführbare Programme für Industrieroboter übersetzt. Dadurch können roboterbasierte Montageprozesse innerhalb von Minuten auf neue Produkte angepasst werden, ohne dass die Mitarbeiter über Kenntnisse des Programmierens oder der Robotik verfügen müssen. Ein Mitarbeiter muss den Montageprozess nur einmal vormachen. Das ARAS-System ermöglicht die kosten- und zeiteffiziente Integration und Adaption von Industrierobotern in der Montage für große und mittelständische Unternehmen. Y1 - 2021 SN - 2364-9208 VL - 37 IS - 6 SP - 23 EP - 26 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 - Schou, Casper A1 - Colli, Michele A1 - Berger, Ulrich A1 - Heidemann Lassen, Astrid A1 - Madsen, Ole A1 - Møller, Charles A1 - Vejrum Wæhrens, Brian ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Stoettrup Schioenning Larsen, Maria ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - Deconstructing Industry 4.0: Defining the Smart Factory T2 - Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021 N2 - The advent of the industrial digital transformation and the related technologies of the Industry 4.0 agenda has uncovered new concepts and terminology in the manufacturing domain. Clear definitions represent a solid foundation for supporting the manufacturing research community in addressing this field consistently. This paper addresses this need focusing on the “smart factory”. Starting from a review of the extant literature and integrating it with the outcome of a Delphi study, we propose a new definition of a “smart factory” and discuss its key characteristics. These are related to interconnectivity capabilities and adaptability to the surrounding environment in order to generate and appropriate value. Eventually, such characteristics are exemplified in an empirical context. The aim of this paper is to provide the research community with an updated definition of a smart factory taking both industrial and societal values into account. Furthermore, it may represent a reference for practitioners engaged in the digital transformation of their factories. Y1 - 2021 UR - https://link.springer.com/book/10.1007/978-3-030-90700-6 SN - 978-3-030-90699-3 U6 - https://doi.org/10.1007/978-3-030-90700-6_40 SP - 356 EP - 363 PB - Springer CY - Cham ER - TY - GEN A1 - Borck, Christian A1 - Schmitt, Randolf A1 - Berger, Ulrich A1 - Hentschel, Christian ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Stoettrup Schioenning Larsen, Maria ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - Development of a IIoT Platform for Industrial Imaging Sensors T2 - Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021 N2 - In the industry, connecting machines and tools - also known as the industrial Internet of things (IIoT) - is an essential part of the digital transformation of a company. The aim is to increase the efficiency and predictability of complex processes. In manual and semi-automatic processes, imaging sensors can help to monitor conditions, gives automated feedbacks to a central system, and e.g. provide current information for a digital twin. However, when imaging sensors are integrated into established IIoT platforms, they quickly reach their system limits due to the multidimensionality and high update and data rates. This paper presents a software platform that enables decoupled automated image processing through the abstraction and contextualization of the sensor technology and its data as well as a plugin architecture. Analogous to edge computing, partial processing can already be performed close to the sensor node to condensate data and reduce network loads and latencies. Thereby, all these approaches increase the longevity, flexibility and scalability of multi-sensor systems and associated processing algorithms. Based on the generic structure of the sensor network, the user is provided with an intuitive user interface that is based on IIoT platforms and enables the integration of their processing pipelines even for non-experts, despite the high complexity of the data. Y1 - 2021 SN - 978-3-030-90699-3 SN - 978-3-030-90702-0 U6 - https://doi.org/10.1007/978-3-030-90700-6_37 SP - 330 EP - 338 PB - Springer CY - Cham ER -