Related papers: Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation

Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation

URL: http://arxiv.org/abs/2207.04515v1
Date: Sun, 10 Jul 2022 18:51:12 GMT
Title: Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation
Authors: Holger Eichelberger, Gregory Palmer, Svenja Reimer, Tat Trong Vu, Hieu Do, Sofiane Laridi, Alexander Weber, Claudia Nieder\'ee, Thomas Hildebrandt
Abstract summary: 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.
Score: 47.37985501848305
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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