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We are currently experiencing rapid transformation in technologies and society. Due to the convergence of various megatrends, these changes have considerable impacts on everyday life. Our study aims to identify relevant strategies for the digital future of a macro-region (Tyrol, South Tyrol and Veneto). The study conducts semi-structured interviews with representatives of companies, universities and local governments, using the approach of a triple helix model. Based on the empirical analysis, we develop an action plan for the digital transformation of the macro-region.

In human-robot collaboration (HRC), in which the employee works next to the robot - as is often the case in the previously purely manual assembly - the cost effectiveness of HRC application is often difficult to represent. Therefore, in the design of HRC applications, the focus in the first planning phase is on ensuring economic efficiency. In the ROKOKO research project, the involved partners developed a simple method for estimating the required total investment. The planning of a HRC application case at the company Metabowerke GmbH using the new method is the subject of this article.

Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.

Artificial intelligence (AI) and self-learning systems have immense economic potential and are a driving force for digitalisation. Artificial Intelligence is radically changing value chains, business models, and employment in industry. Data-driven services are added to traditional products in almost all industries. Integrating Artificial Intelligence in products and services as well as using data from the production process provides opportunities for new business models in an increasing competitive international environment.

Today’s digital factory needs a large variety of different robot systems. For this purpose, the paper presents an approach which assembles the control software with standard function blocks for different axis groups and tests it with a digital twin. The robot simulation is based on the gaming software UnReal and can easily be adapted to different kinematics by specifying the Denavit-Hartenberg parameters. After successful virtual commissioning, the robot hardware is assembled as modularly as the control software by means of individual axes.

The use of autonomous systems is not efficient in all applications due to variable system environments or small quantities. Semi-autonomous systems are able to bridge this gap. This article presents a digital twin-based approach for human-machine interaction using adaptive automation. A case study shows how a modular digital twin can support the operator of a CPS in specific tasks. This method allows for a distinction between short-term signal changes and long-term behavior modification. Thus, semi-autonomous systems can support operators in scenarios in which autonomous systems are not viable.

The paper analyzes older people’s expectations and perceptions about welfare technology and in particular about assistant robots in elderly care. Assistant robots may extend autonomy in old age and provide support for caregivers. In this study attitudes of older people, caregivers and care managers were collected through focus group discussions, by exploring seven categories that need to be addressed to improve orientation towards assistant robot technology in care. Therefore, an adequate dissemination of information may enhance people’s acceptance and reduces fear toward technology in care.

Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.

Everyone is talking about artificial intelligence and machine learning. However, knowledge about what the terms actually mean is often not yet extensively available. The article presents some basic knowledge and shows which application possibilities and added values machine learning can offer for production. Robotics, for example a bin-picking system, benefits in particular from the technologies described. Finally, the article deals with the topic of explainability of machine learning processes. For technical, legal and social reasons, decoding the “black box” is an essential task.

Sophisticated port infrastructure systems often have a significant potential for increasing energy efficiency and optimising internal processes. Supported by intelligent and innovative methods, solutions are to be created to improve existing procedures without having to make large-scale changes to the port infrastructure. The specific application scenario of intelligent processes is a tidal water port in Northern Germany.