Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications

2025-10-05

IEEE International Conference on Systems, Man and Cybernetics

AI Summary

This work addresses the challenges of efficiently deploying and reusing AI/ML solutions in industrial cyber-physical systems, which are hindered by heterogeneous protocols, data formats, and devices. To overcome these limitations, the authors propose a modular and interoperable architecture based on digital twins, introducing the novel concept of a “zero-configuration AI pipeline.” In this framework, the digital twin serves as an intelligent orchestrator that integrates semantic data modeling with modular machine learning, enabling decoupling and automated integration of AI components with physical systems. The approach supports dynamic deployment of AI functionalities, concurrent execution of multiple models, and cross-platform reuse. Experimental validation in a MicroFactory scenario demonstrates that the proposed solution significantly enhances the deployment efficiency and flexibility of intelligent services in complex industrial environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Efficient ML / Green AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.

Problem

• Cyber-Physical Systems
• Digital Twin
• AI integration
• Industrial IoT
• Interoperability

Innovation

• Digital Twin
• Zero Configuration AI
• Interoperable AI Pipelines
• Cyber-Physical Systems
• Industrial AI