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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.
Schmitt et al. provide a brief overview of the key technology for Industry 4.0, the Industrial Internet of Things (IIoT). For this purpose, the paper first examines how this technology has developed, what its essential components are and what challenges still need to be overcome. The work particularly presents the specific requirements, challenges and opportunities for SMEs. “Industrial Internet of Things (IIoT)” concludes with an example about the integration of IIoT in a model factory.
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.
The Digital Twin is seen as the step towards digitalization in many areas, including the area of manufacturing which is the focus of this paper. However, the frequently application-oriented development of concepts and initial implementations make it difficult to adapt them to other applications. Especially when production deviates from the usual highly automated processes and requires high flexibility, e.g. to allow batch size one. In such a case, the Digital Twin must support modular shopfloors with flexible areas in which different production times and various tools and machines are used. It should also be possible to react promptly to problems in production. Design errors that only become apparent during production must be reported directly. In order to deal with this problem, the paper presents a concept for an abstract representation of a Digital Shadow (DS). The DS combines the disciplines of production planning and control, product lifecycle and layout planning which creates new links and enables further application scenarios. Based on this Digital Shadow, different applications can be realized that provide user-related solutions. For this purpose, a tree structure is used that links information about the product and the production environment which in this case is variable.
Star Wars in food stores -automated detection, determination and laser elimination of insect pests
(2018)
In a project supported by funds of the German government (PT BLE), we test a mobile camera system, scanning surfaces in storage warehouses or food processing industry. If insects are detected they are compared with morphological data in store to decide if the detected individual is a target pest. In case a target pest is determined with high probability, a laser beam is directed to the target to eliminate the insect by heat. The concept is to develop a system that is able to learn and identify more and more different species over time. First aims of the project are to improve reliability of species detection and identification in contrast to the grain with different light spectra and camera parameters. Reaction tests under different light conditions of the two exemplary insects grain weevil Sitophilus granarius (Col., Curculionidae) and Indianmeal moth Plodia interpunctella (Lepid., Pyralidae) will be carried out. Further the project will investigate laser beam wavelengths and intensities not damaging surfaces and items beneath or next to targets. The system could be utilized to support IPM in well-sealed structures for storage or processing of food and feed stuffs.