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Reference architecture for the design and implementation of AI systems in manufacturing in conformity to ISO/IEC 42001
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DOI：10.1080/0951192X.2026.2664187.png)
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Abstract
En 中文
The increasing adoption of artificial intelligence (AI) and machine learning (ML) in manufacturing is driven by the need for more personalised, efficient and adaptive production systems. However, industrial AI systems must also comply with emerging governance frameworks, particularly ISO/IEC 42001:2023, the AI management system standard. Despite its relevance, manufacturers often struggle to translate the standard’s high-level requirements into practical design and implementation decisions.This article proposes a reference architecture to support the design, deployment and governance of AI systems in manufacturing in accordance with ISO/IEC 42001:2023. The architecture defines the main system layers, functional components, and governance mechanisms required to ensure lifecycle control, traceability, operational integration, and alignment with compliance. Given the recent publication of the standard, this work provides an early contribution to operationalising AI management system requirements within industrial architectures. The architecture is applied to a real-world metal manufacturing use case focused on predictive maintenance. The implementation demonstrates its feasibility and improves system traceability and operational performance, including overall equipment effectiveness and maintenance-related downtime. Although validated in a specific context, the modular and layered design supports adaptation to other manufacturing environments, considering domain constraints, available infrastructure and legacy system integration.
Keywords:
Reference architecture
Artificial Intelligence
ISO/IEC 42001
Trustworthy AI
industrial AI
MLOps
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