Pilot Production Environments Driven by Digital Twins

Lutters, Eric

doi:10.7166/29-3-2047

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…Fig. 17 illustrates the roles of digital twin in the context of digital engineering [145,146]. First, the smart product will be designed, and its product model will be stored in the PLM as a digital master.…”

Section: Digital Twinmentioning

confidence: 99%

Development capabilities for smart products

Tomiyama¹,

et al. 2019

Self Cite

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Section: Digital Twinmentioning

confidence: 99%

Development capabilities for smart products

Tomiyama¹,

et al. 2019

Self Cite

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“…The concept of using "twins" comes from the Apollo NASA (National Aeronautics and Space Administration) program; later, it was used also in aviation, such as the "Iron Bird" [96]. Digital twin devices offer a platform for the development, testing, improvement, and upscale of the manufacturing environment [99]. Digital twin technology is considered a key technology for the realization of cyber physical systems [100].…”

Section: Use Of Virtual Reality Simulation and Digital Twinsmentioning

confidence: 99%

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Vrchota

Pech

2019

Applied Sciences

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Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of the paper is to characterize the readiness of the enterprise to use Industry 4.0. In the research, a questionnaire survey was carried out on a sample of 276 enterprises mainly from the manufacturing industry. Using explorative factor analysis, the index of Industry 4.0 (VPi4) was designed to determine the level of Industry 4.0 implementation in the enterprises. The results were further verified by a statistical analysis, using Mann–Whitney test and correlation coefficients. The results indicate that the VPi4 index was consistent in terms of distribution when comparing the results on the verification sample. Its results correlate with the subjective perception of the enterprises, and different levels of the index reflect the difference in technological intensity of the industry. The VPi4 index enables the enterprises to determine their own level of current state of readiness for Industry 4.0, to better prioritize business development. The proposed solution categorizes Industry 4.0 components into a useful theoretical framework. Further research offers the possibility of applying the index in other sectors, its relation to the size of enterprises, and updating with respect to new trends in information technology.

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“…Table 1 lists the applications of digital twin during design phase. [17][18][19][20] In the manufacturing process, device compatibility, algorithm complexity, and security are highly demanded to make the system smarter. A minor failure may cause severe damage to the smart manufacturers.…”

Section: Intelligent Manufacturingmentioning

confidence: 99%

“…Xiao et al 24 transformed semi‐experienced based design into data and model‐based design to promote the traditional experience. Table 1 lists the applications of digital twin during design phase 17–20 …”

Section: Introductionmentioning

confidence: 99%

Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition

Deebak

Al‐Turjman²

2021

Int J of Intelligent Sys

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The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital‐twin for industrial practice. It has low‐power wireless‐enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital‐twin‐assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool‐holder that integrates a k‐type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool‐holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.

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Pilot Production Environments Driven by Digital Twins

Cited by 18 publications

References 17 publications

Development capabilities for smart products

Development capabilities for smart products

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition

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