Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system

Coronado, Pedro Daniel Urbina; Lynn, Roby; Louhichi, Wafa; Parto, Mahmoud; Wescoat, Ethan; Kurfess, Thomas R.

doi:10.1016/j.jmsy.2018.02.002

Cited by 211 publications

(55 citation statements)

References 21 publications

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“…2019, 9, 3325 5 of 24 intelligence. Ubiquitous technology is applied to machines so the machine can be controlled and monitored in real-time [24,39,84].…”

Section: Proposed Model Of Smart Manufacturingmentioning

confidence: 99%

“…In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20][21][22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25][26][27][28][29], agent technology [30][31][32][33][34][35], cognitive technology [36,37], big data [38,39], data mining [40,41], and artificial intelligence [42][43][44][45][46][47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21,48].…”

Section: Introductionmentioning

confidence: 99%

“…The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20][21][22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25][26][27][28][29], agent technology [30][31][32][33][34][35], cognitive technology [36,37], big data [38,39], data mining [40,41], and artificial intelligence [42][43][44][45][46][47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21,48].…”

mentioning

confidence: 99%

“…2019, 9, 3325 5 of 24 intelligence. Ubiquitous technology is applied to machines so the machine can be controlled and monitored in real-time [24,39,84].Human-machine interface (HMI), smart phone, and personal digital assistant (PDA) are used for interfacing the machine with workers. ICT infrastructure such as manufacturing execution system (MES), enterprise resource planning (ERP) [85], wireless network, RFID system [86], and sensor network [87] enables the CPS to capture its status as well as to communicate with other CPSs.…”

mentioning

confidence: 99%

“…2019, 9, x 5 of 26 artificial intelligence. Ubiquitous technology is applied to machines so the machine can be controlled and monitored in real-time [24,39,84]. Human-machine interface (HMI), smart phone, and personal digital assistant (PDA) are used for interfacing the machine with workers.…”

mentioning

confidence: 99%

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Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

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Featured Application: A proposed smart manufacturing system has the ability to adapt to manufacturing changes. Abstract:The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5,6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20]...

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“…2019, 9, 3325 5 of 24 intelligence. Ubiquitous technology is applied to machines so the machine can be controlled and monitored in real-time [24,39,84].…”

Section: Proposed Model Of Smart Manufacturingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process

Rath

Chatterjee²,

Patro

et al. 2023

Intelligent Manufacturing Management Systems

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Convolutional Neural Network‐Based Method for Predicting Oxygen Content at the End Point of Converter

Wang

Bao

2022

steel research int.

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The main factor affecting the cleanliness of steel is inclusions, most of which are deoxidation products. The greater the oxygen content fluctuation at the converter's end point, the more unstable the inclusion control. A large amount of charging, operation, and sampling data is collected during the converter smelting, but it is not fully utilized, and the deoxidizing alloy addition still relies on manual experience. Herein, the representative convolutional neural network (CNN) of deep learning is adopted, and the number of convolutional layers, convolutional kernel size, and the number of convolutional kernels are optimized to establish the best prediction model of the converter end point. Its root mean square error, mean absolute error, and mean absolute percentage error are 35.29%, 25.59%, and 7.30%, respectively, which are superior to the back propagation neural network. This CNN prediction model uses 1300 sets of preprocessed data containing 23 indicator variables, of which 1200 sets of data are used for training and 100 sets for model validation. Within the ±50 ppm error scope, the model prediction hit rate can reach 87%, and when within the ±70 ppm error scope, the hit rate is 93%.

show abstract

Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system

Cited by 211 publications

References 21 publications

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process

Convolutional Neural Network‐Based Method for Predicting Oxygen Content at the End Point of Converter

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