Hybrid digital twin model improves mechanical engineering insight
An Exemplar of a Digital Twin in Mechanical Engineering: Understanding Model Hybridization
Software Engineering
Summary
Digital twins are digital copies of real machines that help engineers predict problems early and improve designs. The authors studied a case where two types of models—one based on physical laws and one based on data—are combined to make better predictions. They described how this combination, called hybridization, was planned and documented in a specific mechanical system. This method helps future engineers understand and recreate similar hybrid digital twins more easily.
What this means in practice
- •For mechanical engineers: Develop hybrid digital twin models that explicitly combine physics and data-driven methods to better predict mechanical system behavior over its lifecycle.
- •For industrial maintenance teams: Use documented hybrid digital twin designs to anticipate equipment issues early and plan maintenance more effectively in mechanical plants.
Authors
Mahussi Datongnon, Hubert Lejeune, Yoann Jus, Benoit Combemale, Julien Deantoni
Abstract
Digital Twins (DTs) are widely adopted across a variety of application domains. In industrial sectors, particularly in mechanical engineering, they accelerate product development, reduce risks, enable early issue prediction, and lower sustainment costs . In practice, DTs increasingly integrate physics-based (deductive) and data-driven (inductive) models into hybrid models combining the complementary strengths of both modeling paradigms. In this paper, we refer to this integration paradigm as hybridization. Despite this trend, the engineering of hybrid DTs that is, remains insufficiently documented. Hybridization is often introduced in an ad hoc manner, and its implementation is only partially made explicit, which limits reproducibility and transferability. This paper reports on the development of an existing fluidic loop digital twin at Centre Technique des Industries M{é}canique (CETIM). The DT is described using the characterization framework of Gil et al., providing a structured view across its lifecycle dimensions. To make hybridization explicit, the case is further analyzed through a complementary characterization structured along two dimensions: motivation and realization. The resulting description provides a traceable account of hybridization decisions and supports the documentation and transfer of hybrid DT engineering practices.