Publikation - Einzelansicht

@inproceedings {42029,
author={Holger Eichelberger and Gregory Palmer and Svenja Reimer and Tat Trong Vu and Hieu Do and Sofiane Laridi and Alexander Weber and Claudia Niederée and Thomas Hildebrandt and Thais Batista and Tomás Bures and Claudia Raibulet and Henry Muccini},
title={Developing an AI-Enabled IIoT Platform - Lessons Learned from Early Use Case Validation},
publisher={ - Springer International Publishing},
address={ - Cham},
booktitle={Software Architecture. ECSA 2022 Tracks and Workshops},
volume={13928},
year={2023},
pages={265-283},
isbn={978-3-031-36888-2},
doi={https://doi.org/10.1007/978-3-031-36889-9_19},
abstract={<p>For a broader adoption of AI in industrial production, adequate infrastructure capabilities and ecosystems are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. IIoT platforms can play a major role here by providing a unified layer for the heterogeneous Industry 4.0/IIoT context.</p> <p>However, existing IIoT platforms still lack required capabilities to flexibly integrate reusable AI services and relevant standards such as Asset Administration Shells or OPC UA in an open, ecosystem-based manner. This is exactly what our next level Intelligent Industrial Production Ecosphere (IIP-Ecosphere) platform addresses, employing a highly configurable low-code based approach.</p> <p>In this paper, we introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.</p>}
}