Interoperability of AI-enhanced digital twins

Interoperability is one of the biggest challenges when multiple digital twins are used in collaboration. Although attempts to standardise and define interfaces have made significant progress, real interoperability is still difficult to achieve. It is due to unstated assumptions, contextual factors, and quality characteristics not covered by conventional methods. This paper presents a composition framework that uses a meta-model to capture contextual factors and quality characteristics in a structured manner that is required for compatibility between the models. It is achieved by developing a meta-model that explicitly represents the quality characteristics that can be used to decide whether digital twin models can be validly composed. Validation of the approach is illustrated by examples showing how our approach identifies the issues that are otherwise hidden compatibility issues. This paper also provides an algorithm to provide reasoning logic for requirements assessment by making implicit assumptions and contextual factors explicit and enabling the composition of digital twin models to be more effective.