Asset administration shells compared and assessed for manufacturing use
A Set-Theoretic Evaluation Framework for Assessing Asset Administration Shell Instances: Towards Comparability and Suitability
Software Engineering
Summary
In manufacturing, Asset Administration Shells (AAS) store information about machines and parts in a standard way, but different AAS versions can look very different and have missing info. To help decide if an AAS is right for a job, the authors developed two methods: one to compare different AAS versions by looking for what matches or is missing, and another to check how well an AAS fits a specific use case by looking at structure, meaning, and completeness. This makes it easier for companies to choose or improve AAS for software that runs manufacturing processes.
What this means in practice
- •For manufacturing software developers: Compare and verify Asset Administration Shells to ensure they meet specific software service requirements.
- •For industrial asset managers: Assess different digital representations of physical assets to select those suitable for particular manufacturing processes.
Authors
Carsten Ellwein, David Dietrich, Rozana Cvitkovic, Bastian Lang, Hansjoerg Tutsch, Andreas Wortmann
Abstract
Asset Administration Shells (AAS) provide a standardized means of representing assets and their information in manufacturing and increasingly serve as a basis for software services. However, different AAS instances vary in structure, content, and degree of completion, making it difficult to determine whether a given AAS is suitable for a specific application. This paper presents two complementary methods to support the comparison and application-oriented assessment of AAS. First, set-theoretic operations are employed to compare AAS models, enabling the identification of common, missing, and differing submodels and parameters. Second, an AAS suitability model assesses the conformity of an AAS to the requirements of a specific use case. The assessment considers structural conformity, semantic consistency, cardinality, and specification conformity and can be performed either against a reference AAS or a set of required SemanticIDs. A suitability value is derived from the identified deviations and is complemented by a detailed report of missing or non-conforming information. The proposed approach support practitioners and researchers in the comparison of evolving AAS and provide application-specific information on their suitability for manufacturing software services.