Co-Creation

Collaborative Robots in Production Environments

Collaborative Robots in Production Environments

Employee qualification and acceptance for human-machine interaction
Tobias Wienzek, Mathias Cuypers ORCID Icon
The introduction of new technologies poses a major challenge, especially for small and medium-sized enterprises (SMEs). At the same time, SMEs must rise to this challenge in order to keep pace technologically and economically. Employee acceptance is an important factor in ensuring that both the introduction and the long-term use of a technology are successful. At the same time, the introduction process also has a central influence on acceptance in the long term. This article uses the implementation of collaborative robotics as an example for examining such an introduction process, identifying the key factors that influence employee acceptance and the important role played by advanced employee training. It serves to highlight how the introduction process and employee training are seamlessly interlinked.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 14-21 | DOI 10.30844/I4SE.26.2.14
Multi-Stakeholder AI Ethics in Radiology

Multi-Stakeholder AI Ethics in Radiology

Implications for integrated technology and workplace design
Valentin Langholf ORCID Icon, Alexander Ranft, Lena Will, Robin Denz ORCID Icon, Johannes Schwarz ORCID Icon, Majd Syoufi, Pavlos Rath-Manakidis, Marc Kämmerer, Marcus Kremers, Axel Mosig ORCID Icon, Uta Wilkens ORCID Icon, Jörg Wellmer ORCID Icon
AI assistance can be seen as a welcome aid in radiology, a highly complex environment characterized by round-the-clock time pressure and quality expectations. However, it must meet high ethical standards from the perspective of both users and patients. It is a challenge to incorporate this human-centered approach into the development and introduction of AI applications.
Industry 4.0 Science | Volume 42 | Edition 1 | Pages 136-143 | DOI 10.30844/I4SE.26.1.128
Artificial Intelligence in Visual Quality Control

Artificial Intelligence in Visual Quality Control

Using intelligent algorithms to improve product quality, increase efficiency and reduce costs
Stefanie Horrmann
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.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 57-60