Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation
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Abstract (arXiv)
For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. 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. 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 37 references that are in the map. First posted on arXiv 10 Jul 2022.
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6 of 37 references.
References · 6
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2021Pre-Trained Models: Past, Present and Futurecited by 319
- 2022Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognitioncited by 6
- 2019Fully Convolutional One-Shot Object Segmentation for Industrial Roboticscited by 2
- 2020The Automated Inspection of Opaque Liquid Vaccinescited by 1
- 2022Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learningcited by 1
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