Radio frequency identification–enabled monitoring and evaluating in the discrete manufacturing process

Yuan, Liuyin; Guo, Yu; Wei, Fangjian; Lü, Kun; Jiang, Jiajun; Nian, Liyun

doi:10.1177/0954405415620986

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…77 Use of RFID for tracking work-in-progress in shop floor has also been shown. 78 Tracking and tracing may also be used to provide data regarding location; and if that data are used for analysis, then it could be considered as a part of the data analytics cluster as well. Tracking and tracing could also be replaced by tracking and tracing in real time , as we can track the location of objects in the real time.…”

Section: Characteristics Clustersmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

405

261

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The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of ''semantic similarity'' to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

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Section: Characteristics Clustersmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

405

261

View full text Add to dashboard Cite

show abstract

“…In the discrete manufacturing system, the process monitoring was achieved by the radio frequency identification to capture the real-time information for developing the scheduling simulation model. 28 The new simulation optimization tool of DES was used in many scenarios, such as prediction and evaluation of intractable production questions for the decision-makers to reduce the economic cost. 29 Turner et al 30 constructed a test platform using DES to make decisions for smart factories.…”

Section: Literature Reviewmentioning

confidence: 99%

Process optimization through closed-loop Kaizen with discrete event simulation: A case study in China

Ming

Qiu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This article presents a closed-loop Kaizen system with discrete event simulation in the stage of design and reengineering of mass-customization production system. This approach contains four steps: current process analysis, production line design, discrete event simulation to solve the dynamic event questions, and implements and evaluation. The current process analysis and the production line design refer to the value stream mapping and the productivity analysis based on Kaizen principle, respectively. Production line designs are the modeling basis of discrete event simulation, and discrete event simulation feeds back results to the decision-makers to reduce waste and cost and improve the quality and productivity by reforming the production line. Therefore, by connecting the closed-loop Kaizen with discrete event simulation, the production line can be continuously and automatically improved. To demonstrate the potentials of the closed-loop Kaizen system, a case study was applied based on the original production layout and economic benefits were achieved. Moreover, this article indicates a direction for the full automation of process optimization.

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“…Mori and Fujishima 17 developed a remote monitoring and maintenance system for computer numerical control (CNC) machine tool monitoring using mobile phones or PCs. Yuan et al 18 presented an RFID-based visibility monitoring framework for work-in-process tracking in the discrete manufacturing process. Grisostomi et al 19 designed a WSN and installed it in a manufacturing cell.…”

Section: Literature Reviewmentioning

confidence: 99%

A resource-oriented middleware in a prototype cyber-physical manufacturing system

Liu

Jiang

Zhang

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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The interconnection among heterogeneous sensors and data acquisition equipment in cyber-physical systems have profound significance in achieving adaptability, flexibility, and transparency. Various middlewares have been developed in cyber-physical systems to collect, aggregate, correlate, and translate system monitoring data. Existing middleware solutions are normally highly customized, which face several challenges due to the highly dynamic and harsh production environments. The data generated by sensors can only be shared by specific applications, which prevents the reusability of sensors. Moreover, the lack of uniform access to sensors causes high cost and low efficiency in application development. To address these issues, a resource-oriented middleware architecture called ROMiddleware was proposed, and three key enabling technologies including heterogeneous sensor modeling and grouping, open application programming interfaces development, and token-based access right control mechanism have been developed. Under the guidance of the key enabling technologies, a prototype of ROMiddleware was implemented and its performance was evaluated. Finally, two applications were developed to stress the significance of ROMiddleware. The results show that ROMiddleware can meet the requirements of data acquisition in cyber-physical systems.

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Radio frequency identification–enabled monitoring and evaluating in the discrete manufacturing process

Cited by 10 publications

References 23 publications

Smart manufacturing: Characteristics, technologies and enabling factors

Smart manufacturing: Characteristics, technologies and enabling factors

Process optimization through closed-loop Kaizen with discrete event simulation: A case study in China

A resource-oriented middleware in a prototype cyber-physical manufacturing system

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