Harmonising and integrating the Digital Twins multiverse: A paradigm and a toolset proposal

Cimino, Chiara; Ferretti, Gianni; Leva, Alberto

doi:10.1016/j.compind.2021.103501

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…The objective is to allow DTs to be interlinked and exchange information in the same way as their real-world POs. Cimino et al [ 93 ] propose a paradigm called the Digital Multiverse to comprehend DT interpretations at the data integration level and also by establishing and enforcing consistency rules. For that, they create relations that involve both data, models and the different system life-cycle management methods.…”

Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

129

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A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

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Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

129

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“…Its role in the realm of big data analytics for future smart factories has been highlighted (Gao et al, 2020), along with its potential for improving quality and efficiency in the casting industry (Sun et al, 2021). Other studies focus on the use of synthetic data for scene text recognition (Zhang et al, 2021), performance enhancement of industrial systems (Bécue et al, 2020), object recognition and tracking (Godil et al, 2013), and harmonizing digital twins (Cimino et al, 2021).…”

Section: Multidimensional Importance Of Synthetic Datamentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

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“…( 10 ). In 2018, Cimino et al ( 11 ) described the DT as “a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle.” In the industrial field, it can build virtual copies of products or workshops through Big Data, which can real-time interact with the deep information and physical space we cannot see normally. Qi et al ( 12 ) put forward the concept of the DT workshop driven by the fused twin data, which can realize iterative operation and optimal management, planning, and control of the workshop.…”

Section: Introductionmentioning

confidence: 99%

The Digital Twin in Medicine: A Key to the Future of Healthcare?

Sun

Song

et al. 2022

Front. Med.

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There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.

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Harmonising and integrating the Digital Twins multiverse: A paradigm and a toolset proposal

Cited by 18 publications

References 26 publications

Design, Modeling and Implementation of Digital Twins

Design, Modeling and Implementation of Digital Twins

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

The Digital Twin in Medicine: A Key to the Future of Healthcare?

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