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Institute
Durch die Menschzentrierung in der Industrie 5.0 [1] sollten bestehende Ansätze hinterfragt und geprüft werden [2]. Gerade der, durch die Digitalisierung hervorgerufene, Technostress sollte neben dem Fachkräftemangel einen zunehmenden Betrachtungsfaktor im Produktionsumfeld erhalten. Da trotz bisheriger Maßnahmen eine stetige Zunahme von Stress zu verzeichnen ist [3]. Auffällig sind dabei die Arbeitsunfähigkeits-Fehltage, welche im letzten Jahr um weitere 6 % bzw. um 18,2 Fehltagegestiegen sind. Womit durchschnittlich jede Erwerbsperson 3,17 Tage unter der Diagnose „psychische Störung“ krankgeschrieben wurde [4]. Um diesen Trend entgegenzuwirken, betrachtet der Beitrag die Möglichkeiten der Software-Ergonomie, womit die Gestaltung von HMIs (Mensch-Maschinen-Interfaces) den Menschen gezielter berücksichtigt um etwaige Belastungen zu reduzieren [5]. Im Rahmen der komplementären Industrie 4.0 und der einhergehenden Informationsflut, bestimmen immer komplexer werdende Schnittstellen und Bedienoberflächen einen Großteil des Arbeitsalltags. Diese ermöglichen den Anwenderinnen und Anwendern neben einem schnellen Überblick über Maschinendaten, Prozessabläufe und Handlungsempfehlungen die Möglichkeit zur einfachen Bedienung der technischen Anlagen. Der hier vorgestellte Ansatz betrachtet am Beispiel einer Bestandsanlage, wo sich Potenziale in der Stressreduzierung im Bereich der visuellen Benutzerschnittstellen befinden und welche Auswirkungen eine Veränderung der Leitführung ermöglicht. Hierfür wurde eine mehrphasige Studie durchgeführt dessen Ergebnisse im Bereich des Eye-Trackings sowie anhand von Vitalwerten im vorliegenden Beitrag diskutiert werden.
The Smart Production Vision
(2022)
In this chapter, the Smart Production vision is discussed. The Smart Production approach is developed and described, and Smart Production is positioned in relation to Industry 4.0. Smart Production operationalize the journey towards Industry 4.0 and beyond. First, the need for a new approach to manufacturing is discussed, and from the perspectives of Industry 4.0, the Smart Production concept is derived. Then the framework is explored and finally, the approach is outlined. The Smart Production vision is an approach to make an integrated production system smarter by continuous digitizing, automating, and organizing towards supporting the company specific missions.
This chapter will introduce the second part of the book. This part contains a collection of chapters aimed at supporting the SMEs in the transformation toward the Smart Production vision. In this part, different approaches are presented, which can assist SMEs in the formulation of a smart production vision and in the identification and prioritization of relevant initiatives, guiding the outline of a project roadmap. Furthermore, the part will introduce different regional innovation platforms in Denmark and Germany which support the SME transformations. Finally, it will be discussed how subscription-based methods could be used by SMEs to cut upfront investments and reduce requirements for digital competencies.
Borck et al. evaluate the challenges and opportunities of Industrial Internet of Things (IIoT) and smart sensors in human-centered manufacturing. Particularly in small and medium-sized manufacturing with fewer machines and smart tools, it is significantly more difficult to automate processes and get the required information from the shop floor. Therefore, they give proven recommendations for the use of sensors based on a set of frequently occurring tasks in assembly, maintenance and logistics to achieve the support of smart data models in the context of Industry 4.0. “IIoT and smart sensors in human-centered manufacturing” concludes with concrete sample scenarios and describe the challenges and one solution using smart sensors and data models.
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.
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.
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.
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.
This minitrack will address scientific and technical research on how traditional production systems are transformed into next generation smart production systems. It comprises a mix of presentations from leading industries (automotive and aircraft) as well as from well-known research institutions (Fraunhofer society, Germany) and universities (Brandenburg University of Technology Cottbus-Senftenberg, University of Bergamo, Aalborg University).
The minitrack will foster discussions concerning the strategies companies are adopting for managing such transformation, the need for new competences for sustaining it, the implementation of new digital technologies and their impact on the existing production systems.