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Journal Articles Computers in Industry Year : 2024

A Digital Twin use cases classification and definition framework based on Industrial feedback

Abstract

The Digital Twin paradigm is a very promising technology that can be applied to various fields and applications. However it lacks a unifying framework for use cases’ classification and definition. The goal of this paper is to address the identified gap. Using a field study and a bottom-up approach, it aims at categorizing the various usages of industrial Digital Twin to help formalize the concept and rationalize its adoption by a range of industrial sectors. The study is based on an iterative process of collecting use cases from a wide variety of verticals, applying grounded theory principles. The usage scenarios were extracted, synthesized, grouped and abstracted to develop an actionable use cases classification framework. This article presents the resulting taxonomy and illustrates it by detailing real industrial use cases, including their added value and their fields of application. This use cases collection, classification and analysis led to a study of the common aspects proposed in academic and industrial definitions of Digital Twin. The goal was to combine and generalize these aspects into a pragmatic and unifying definition, on which the Alliance for Industry of the Future committee has converged. The main contributions of this work include proposing, from a joint industrial and academic perspective, i) the first domain-independent and industry-focused systematic collection of use cases of the digital twin technology, ii) a comprehensive framework to analyse and classify the digital twin use cases and their requirements, and iii) a consensual general definition of the industrial digital twin to contribute to the structuring and standardization of this very active ecosystem.
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cea-04607213 , version 1 (10-06-2024)

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Emmanuelle Abisset-Chavanne, Thierry Coupaye, Fahad Golra, Damien Lamy, Ariane Piel, et al.. A Digital Twin use cases classification and definition framework based on Industrial feedback. Computers in Industry, 2024, 161, pp.104113. ⟨10.1016/j.compind.2024.104113⟩. ⟨cea-04607213⟩
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