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The main goal of assembling a self-sufficient microgrid is to integrate all technical equipment into an autonomous energy supply system as a virtual power plant (VPP). The system integration focuses on the power electronic devices and the combination of gas and electrical supply chains. The developed microgrid structure is fed from renewable energy systems (green hydrogen), the electrical grid and the H2 gas grid with a liquid or gaseous energy source. In comparison to an island grid, the microgrid can be operated in parallel mode with the common public grid. The associated challenges of transient energy flows and the holistic view of a regulated microgrid based on an electrical grid and an H2 gas network are part of this article.

Holistic production systems (HPS) do not only produce positive effects in large companies - they also have an impact on small and medium-sized enterprises (SMEs), such as improved adherence to delivery dates. However, HPS cannot be copied from large companies to SMEs due to different initial situations and circumstances. The introduction of HPS means a high effort for SMEs. In this paper, an approach is presented on how a bionic principle can make HPS less costly and at the same time more effective for SMEs.

Despite a strong media presence and proven potential benefits, Industrie 4.0 is not yet established in many companies. Industry 4.0 projects often remain in a prototype status and do not deliver long-term added value. The solution is an integrated digital system landscape consisting of a connected, digital infrastructure and a business organization oriented towards Industrie 4.0. Through a four-step approach, this paper presents the foundations that need to be created to enable scalable solutions and realize long-term benefits.

Artificial intelligence (AI) is the next stage in the digitalization of the economy. The technology also offers great potential for small and mediusized enterprises (SMEs). However, many SMEs are still reluctant to introduce AI and are only at the beginning of digitization: only around one fifth of all SMEs in Germany have thoroughly digitized their own processes and departments. What does this mean for the use of AI in companies? What steps should businesses take now to take advantage of the opportunities AI offers? And what stumbling blocks should be avoided? This article presents practical implementation concepts for companies with different levels of digital maturity and AI deployment capabilities and shows the range of potential benefits of AI applications in different industries and with different value creation architectures in medium-sized companies.

Visual inspections of product surfaces are predominantly carried out by employees, whereby automation approaches with camera and image processing systems show great potential. Cobots are also being incorporated into quality assurance processes. In the following, the integration possibilities of cobots in visual inspection are discussed and a decision model is presented that can be used to check visual inspection processes for their cobot suitability. The decision model is designed for direct integration into already existing cobot suitability inspection processes and serves as an initial strategic decision-making aid.

Companies, especially SMEs, face the challenge of introducing digital technologies efficiently and as smoothly as possible. Using the introduction of a digital assistance system in technical service as an example, this article shows which challenges and problem areas arise, how they can be overcome and which factors promote a successful introduction process. In the process it is worked out how SMEs with few resources can generate a high degree of participation and acceptance. The basis is a socio-technical understanding that takes a holistic view of the overall system of people, technology and organization in the introduction process of digital technologies.

Data plays an important role in our world - including production technology. Businesses are faced with rising customer demands and competitive pressure. Furthermore, the trend towards smaller batch sizes and increasing variant diversity requires quick reactivity and agility. In order to make the right decisions under these circumstances, data must be generated and analyzed to derive insights. AI technologies are suitable to address the growing uncertainty and complexity. In the following, methods are described that are vital to master data processes for high-quality AI technologies.

The increasing shortage of IT specialists requires lower-skilled employees to be empowered to perform tasks that previously required the involvement of experts. Industry 4.0’s emerging technologies for human-technology interaction and for the digital twin allow the design of intuitive user interfaces, system-independent communication interfaces, and user-specific assistance functionalities to meet this challenge. This paper presents a framework for configuring and monitoring of process flows for different production and logistics systems. By reviewing existing programming approaches, the paper derives requirements for the framework, describes its general architecture and the technical realization of the modular interaction interface. A prototypical implementation validates the presented concept on the example of a cellular conveyor system and a collaborative robot system.

Deep learning-based error detection methods outperform traditional methods because of the continuously increasing complexity of technical systems and inherent flexibility and scalability of Deep Learning techniques. This article introduces the KrakenBox – an autonomous Deep Learning-based error detector for industrial Cyber-Physical Systems (CPS). It exploits a lightweight, Long Short-Term Memory (LSTM) network capable of online error detection that can be deployed on an embedded platform such as NVIDIA Jetson AGX Xavier or even Google Coral Edge TPU. This article describes the architecture of the KrakenBox and demonstrates its application with two case studies.

In the project ARAS (Advanced Robot Assistance Solution) a robot programming assistant was developed, which allows for automated generation of robot programs for assembly processes. By using a multimodal approach for human-machine-interaction, assembly steps are recognized with machine learning algorithms while a worker is showing the robot how an assembly process is performed. Afterwards, a robot program is generated automatically. This way, new robot programs are created within minutes without the user having any knowledge about programming or robotics.