Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops

Sara Cavallero; Federico Tonini; Tony Chahoud; Giampaolo Cuozzo; Davide Borsatti; Walter Cerroni; Maurizio Fodrini; Riccardo Marini

arXiv · 2610.11934

Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops

Sara Cavallero·Federico Tonini·Tony Chahoud·Giampaolo Cuozzo·Davide Borsatti·Walter Cerroni·Maurizio Fodrini·Riccardo Marini

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Abstract

Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization. However, existing DT solutions typically focus either on industrial processes or communication networks, while lacking an integrated and application-aware perspective. To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture. The feasibility of the proposed framework is experimentally validated through a real-world Proof-of-Concept (PoC) implemented in the BI-REX pilot line, involving a 5G-connected Autonomous Mobile Robot (AMR) transporting hazardous liquids and remotely controlled by an AI-driven application. The proposed DT is used to reproduce the behaviour of the real deployment and to investigate the impact of different placements of the AI application, including on-premise, edge, and remote cloud execution scenarios. Experimental results demonstrate a close agreement between DT predictions and PoC measurements in terms of both network-level metrics, such as Reference Signal Received Power (RSRP) and latency, and end- to-end application metrics, including application-level Round- Trip-Time (RTT). Moreover, the analysis shows how inaccuracies of network modeling can critically affect the feasibility of latency-sensitive industrial control loops, highlighting the potential of integrated DTs as tools for the pre-deployment design and validation of next-generation industrial systems.

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Sara Cavallero, Federico Tonini, Tony Chahoud, Giampaolo Cuozzo, Davide Borsatti, Walter Cerroni, Maurizio Fodrini, Riccardo Marini. 2026-10-08. Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops. https://arxiv.org/abs/2610.11934

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