Towards Data-Driven Digital Twins for Smart Manufacturing

Francis, Deena P.; Lazarova-Molnar, Sanja; Mohamed, Nader

doi:10.1007/978-3-030-65796-3_43

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…The authors' goal was to investigate the methodological, technological, operative, and business aspects of developing and operating Digital Twins for the ramp-up activities of the smart factory cell. In the same perspective, Francis et al [4] studied the needs and opportunities to enable data-driven digital twins in the domain of smart manufacturing. They developed a generic datadriven framework incorporating machine learning and process mining techniques as well as continuous model improvement and validation of automated construction of digital twins for smart factories.…”

Section: Existing Approachesmentioning

confidence: 99%

Digital Twin Framework for Reconfigurable Manufacturing Systems: Challenges and Requirements

Hajjem¹,

Benderbal²,

Hamani³

et al. 2021

IFIP Advances in Information and Communication Technology

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Due to the rapid development of new generation information technologies (such as IoT, Big Data analytics, Cyber-Physical Systems, cloud computing and artificial intelligence), Digital twins have become intensively used in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and detailed Digital Twin framework for Reconfigurable Manufacturing Systems. To investigate related works, this manuscript reviews the existing Reconfigurable Manufacturing Systems Digital Twin frameworks. It also presents a classification of several studies based on the Digital Twin framework features and properties, the used decision-making tools and techniques as well as on the manufacturing system characteristics. The paper ends with a discussion and future challenges to put forward a structured and an integrated Reconfigurable Manufacturing Systems -Digital Twin framework.

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Section: Existing Approachesmentioning

confidence: 99%