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Quality Metrics for LLM-Generated Asset Administration Shells: A Perturbation-Based Evaluation Approach

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

The rapid digital transformation of manufacturing, often referred to as Industry 4.0, relies on seamless interoperability between physical and software assets. A central enabler is the Asset Administration Shell (AAS), a standardized digital representation of such assets. Recent advances in large language models (LLMs) enable the generation of AAS submodels from unstructured sources such as product datasheets but raise challenges for quality assurance. In particular, unexpected errors, the lack of ground truth references, and the absence of standardized quality metrics hinder reliable adoption

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