Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

/pdf/industry-4-0-disruption-and-its-neologisms-in-major-4316cu9gbs.pdf

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

https://hal.archives-ouvertes.fr/hal-02913533/document

A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins

Due to agile product development, production systems have to be flexible and adaptable to meet high quality standards and a high productivity. As a result, set-up processes has to be shorter and more resilient because of the increasing number of variants. To meet future requirements, the processes need to be self-adaptive and reconfigurable at any time. Nowadays, a shift of the automation pyramid to interconnected cyber physical systems can be observed as well as emerging technologies as cloud and edge computing are introduced to production systems. These technologies in combination with the Digital Shadow, which provides information about all production assets, open up potential for an adaptive process control and an overall life cycle data management. For this, the Digital Shadow has to be used not only for the aggregation of data, but also for pushing data back into the system and to control the process. As a result, services in regard to an architecture based on edge computing as an enabling technology for an adaptive production together with the Digital Shadow are presented, implemented and discussed on the basis of an industrial use case.

Control from the Cloud: Edge Computing, Services and Digital Shadow for Automation Technologies*

Predicting hot-strip finish rolling thickness using stochastic configuration networks

Digital Shadow Platform as an Innovative Business Model

Configurators as the basis for the transfer of knowledge and standardized communication in the context of robotics

The assembly of control cabinets is highly affected by manual processes. Available solutions to automate the processes are complex, designed for one dedicated task, and expensive. Hence, they are uneconomically for most enterprises, because the typical company in the domain of control cabinet assembly is a small or medium sized enterprise with a high variance and low lot size production. Low-cost and flexible solutions that are suitable for these companies are not available. In order to design an appropriate solution, we have implemented a distributed control pattern. Within this paper, we present the resulting architecture as well as our implementation, and we discuss the advantages of our approach.

Wiring of Control Cabinets using a Distributed Control within a Robot-Based Production Cell

Automated production of individualized products for teaching I4.0 concepts

Investigation of the automation capability of electrolyzers production

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