erfahrungsbasiertes Lernen

Towards Human-Centered Industrial AI Adoption

Towards Human-Centered Industrial AI Adoption

A reference architecture for machine vision demonstrators
Dominik Arnold ORCID Icon, Florian Bülow ORCID Icon, Bernd Kuhlenkötter ORCID Icon
Despite its potential, the introduction of artificial intelligence (AI) in industry is often delayed, primarily due to perceived complexity, high costs, and a lack of expertise. This article presents a modular demonstrator reference architecture that provides practical, low-cost access to industrial AI applications. Developed within a design science research approach, it specifically supports experimentation, learning, and gradual integration into existing production processes. The focus is on machine vision, implemented using cost-effective hardware and open-source software. Its applicability is demonstrated in three scenarios: quality control, chip classification, and in-company training. Initial evaluations confirm the technical feasibility, didactic relevance, and transferability to a variety of industrial contexts.
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 152-160 | DOI 10.30844/I4SE.25.5.146
The Key to Successful Digitalization

The Key to Successful Digitalization

Development, implementation and benefits of digital twins in Industry 4.0
Andreas Bayha ORCID Icon, Sönke Knoch ORCID Icon, Dirk Schöttke ORCID Icon
The success of technologies depends not only on their innovative strength and acceptance, but also on their management. Decision-makers evaluate factors like technical framework conditions and organizational requirements, with the demand for flexibility adding to the complexity. Industry 4.0 addresses this with networking, transparency and decentralized decisions. Digital twins, which can be implemented with open source software, play a key role.
Industry 4.0 Science | Volume 40 | Edition 4 | Pages 42-49