OpenTwins: An open-source framework for the design, development and integration of effective 3D-IoT-AI-powered digital twins
This addresses the problem of fragmented tools for digital twin development, offering a generic solution for industries like petrochemicals, though it appears incremental as it combines existing technologies.
The paper tackles the challenge of developing reliable digital twins by presenting OpenTwins, an open-source framework that integrates 3D visualization, IoT data, and real-time machine learning, demonstrated through a use case in the Petrochemical Industry 4.0.
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 reliable 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 this paper, we present a generic framework for the development of effective digital twins combining some of the aforementioned areas. 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.