OpenTwins: An open-source framework for the development of next-gen compositional digital twins
Julia Robles, Cristian Martín, Manuel Díáz
Abstract
Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving complex digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization, which are rarely seamlessly aligned in open-source solutions. In this paper, we present an open-source framework for the development of compositional digital twins, i.e., advanced digital twins that link individual entities or subsystems to create a higher degree digital twin, allowing knowledge sharing and data relationships. In this open framework, digital twins can be easily developed and orchestrated with 3D-connected visualizations, IoT data streams, and real-time machine-learning predictions. To demonstrate the feasibility of the framework, a use case in the Petrochemical Industry 4.0 has been developed.