FG Automatisierungstechnik
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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.
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.
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.