PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY
PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY
Date
2025
Authors
Vidal, F.
Pertusa, A.M.
Rodriguez, A.
Precker, C.
Deutz, D.B.
Bracchi, V.
Rebe, A.M.
Penalva, M.
Arkouli, Z.
Babcinschi, M.
Journal Title
Journal ISSN
Volume Title
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Abstract
Over the past few decades, industrial automation has significantly reshaped manufacturing and labour through the introduction of advanced technologies. In recent years, the European manufacturing landscape has undergone a transformative shift, moving beyond efficient, automated, and data-driven production toward a more human-centric, resilient, and sustainable model—a transition known as the Industry 5.0 paradigm.
Industry 5.0 defines the human worker as a pivotal element in production. This paradigm leverages technology to enhance human capabilities rather than replace them, fostering an environment where humans and machines collaborate seamlessly.
This transformation is driven by the adoption of collaborative robots (cobots), exoskeletons, and AI, emphasizing cooperation over full automation. Rather than isolating workers from machinery, Industry 5.0 promotes direct human-machine interaction, where industrial equipment is designed to augment human skills, reduce physical strain, and create safer, more adaptive workspaces.
While this paradigm shift offers significant benefits—enhanced worker safety, increased productivity, and improved well-being—it also introduces new challenges in industrial production. As cobots, exoskeletons, and AI-driven systems become integral to workplaces, ensuring their certification for industrial use is critical. These technologies must comply with rigorous safety standards, risk assessment protocols, and regulatory frameworks to guarantee that human workers remain protected in increasingly complex industrial environments.
Description
License: Creative Commons Attribution 4.0 International
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Citation
Vidal, F., Pertusa, A. M., Rodríguez, A., Precker, C., Deutz, D. B., Bracchi, V., Rebe, A. M., Penalva, M., Arkouli, Z., Babcinschi, M., & Neto, P. (2025). PENELOPE White Paper: NOVEL AI-BASED MACHINERY CERTIFICATION METHODOLOGY. Zenodo. https://doi.org/10.5281/zenodo.15270158 .