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Two complementary methods to support the comparison and application-oriented assessment of AAS are presented, whereby set-theoretic operations are employed to compare AAS models, enabling the identification of common, missing, and differing submodels and parameters.
A novel concept of AAS maturity is proposed that characterizes the extent to which established digital twin criteria are met and thus enabling comparability of AAS instances and enable practitioners and researchers to systematically compare AAS instances and support the identification and assessment of further development steps in the digital twin engineering process.
Unlock the full potential of digital twins by understanding the best software architectures for integrating AI-powered services directly into Asset Administration Shells.