Framework for a digital twin in manufacturing: Scope and requirements

Shao, Guodong; Helu, Moneer

doi:10.1016/j.mfglet.2020.04.004

Cited by 178 publications

(63 citation statements)

References 8 publications

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“…According to Gartner, approximately 75% of the organizations that were implementing IoT projects were already using digital twins [ 16 ]. Clearly, the concept is beginning to gain traction, at least among the early adopters.…”

Section: The Evolution Of Digital Twinsmentioning

confidence: 99%

Digital Twins in Livestock Farming

Neethirajan

Kemp

2021

Animals

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Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

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Section: The Evolution Of Digital Twinsmentioning

confidence: 99%

Digital Twins in Livestock Farming

Neethirajan

Kemp

2021

Animals

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“…The ISO 23247 (Digital Twin Manufacturing Framework) standard [22] provides a generic framework for the use of DTs in manufacturing. The standard provides guidelines and procedures for analyzing modeling requirements, defining scope and objectives, promoting common terminology usage, specifying a generic reference architecture that enables instantiating a DT for a specific use-case, and supporting information modeling of the physical system and information synchronization between a DT and its physical counterpart [23]. The Asset Administration Shell (AAS) framework [24] provides specifications and structures that ensure interoperability and exchange of information about I4.0 components in a meaningful way between partners in the manufacturing value chain.…”

Section: A Digital Twinmentioning

confidence: 99%

A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems

et al. 2021

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Digital Twin (DT) is an emerging technology that has recently been cited as an underpinning element of the digital transformation. DTs are commonly defined as digital replicas of components, systems, products, and services that receive data from the field to support intelligent decision-making. Although several frameworks for DT application in manufacturing have been proposed, there is no systematic methodology in the literature that supports the development of scalable, reusable, interoperable, interchangeable, and extensible DT solutions, while taking into account specific manufacturing environment needs and conditions. This paper introduces a DT solution development methodology as a generic procedure for analyzing and developing DTs for manufacturing systems. The methodology is based on the well-known System Development Life Cycle (SDLC) process and takes into consideration: (1) the specificity of DT characteristics and requirements, (2) an understanding of the manufacturing context in which the DTs will operate, and (3) the object-oriented aspects required to achieve DT capabilities of scalability, reusability, interoperability, interchangeability, and extensibility. A case study illustrates the advantages of the proposed methodology in supporting manufacturing DT solutions.

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“…They include: the need to determine what information is needed for decision making (and how often); how to determine the proper level of fidelity for the simulations; and methods to validate probabilistic simulations. NIST provides important resources to the manufacturing ______________________________________________________________________________________________________ This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8348 community through engagement in standards development organizations and publishing the scope and requirements for a digital twin in manufacturing framework [147,148]. Development of AI, machine-vision systems (MVS), and advanced sensor technologies enable the development of highly flexible industrial robots that become more sophisticated and, in some cases, more autonomous.…”

Section: Implications For Nistmentioning

confidence: 99%

National Institute of Standards and Technology Environmental Scan 2020

Evans¹,

Greene²,

Healy³

et al. 2021

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The 2020 National Institute of Standards and Technology Environmental Scan provides an analysis of key external factors that could impact NIST and the fulfillment of its mission in coming years. The analyses were conducted through four separate lenses: Societal, Investment & Geopolitical, Political & Policy, and Technology & Science. Most of the issues discussed in the initial environmental scan conducted in 2018 are still relevant, but a number of key issues have emerged in society that will impact NIST since the writing of that report, most notably the COVID-19 pandemic and the national discourse around race. Societal, political, financial, and workforce issues will challenge NIST to maintain and advance its leadership in metrology, standards, and technology. However, a broad range of emerging technology issues present opportunities for NIST to have significant impact in advancing the nation's innovation and industrial competitiveness.

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Framework for a digital twin in manufacturing: Scope and requirements

Cited by 178 publications

References 8 publications

Digital Twins in Livestock Farming

Digital Twins in Livestock Farming

A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems

National Institute of Standards and Technology Environmental Scan 2020

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