Performance analysis of the RFID system for optimal design of the intelligent assembly line in the learning factory

Gjeldum, Nikola; Mladineo, Marko; Crnjac, Marina; Veža, Ivica; Aljinović, Amanda

doi:10.1016/j.promfg.2018.03.162

Cited by 20 publications

(5 citation statements)

References 11 publications

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“…The authors of [17] report the integration of an RFID-enabled Smart Factory concept within the LF at the University of Split. The same LF also conducted a performance analysis of an RFID system, to highlight some of the limitations of RFID technology [18]. As results, it has been underlined that RFID technology is less suitable for high demand of data transfer, caused by the low range of the communication field and the limited time during which the tag and the reader are close enough to successfully exchange data.…”

Section: Rfid In Learning Factoriesmentioning

confidence: 99%

Using RFID and Industrial IoT in Manual Assembly for Data Collection and Processing in Learning Factories

et al. 2022

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Section: Rfid In Learning Factoriesmentioning

confidence: 99%

Using RFID and Industrial IoT in Manual Assembly for Data Collection and Processing in Learning Factories

et al. 2022

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“…In formula (1), AC represents the assembly situation model, SubC i represents the i-th assembly sub-scenario; in formula (2), itemC i represents the i-th assembly context item.…”

Section: Model Constructionmentioning

confidence: 99%

“…This series of changes gave birth to the emergence and development of new manufacturing models. The new industrial revolution, called industry 4.0, is based on the development of information and communication technology, which brings new challenges to the scientific community and industry, but also specific challenges to demonstrate this new industrial platform in a learning factory environment [1]. An important and interesting theme in the evolution of industry 4.0 is the use of RFID (radio frequency identification) systems to track manufacturing performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Key Technologies of Intelligent Assembly System for Mechanical Products

2021

IJFET

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Manufacturing is country's pillar industries and the core driving force of national economic development. Among them, the machinery manufacturing industry provides technical equipment for the development of the whole country, which plays a decisive function of the development of the national economy and the progress of science and technology. As the increased competition in the global market, the machinery manufacturing industry is facing unprecedented severe challenges. Assembly has a great impact on product quality and the characteristics of complex operating processes, as it is the last part of the mechanical product manufacturing process and therefore often determines the level of mechanically manufactured products. The purpose of this paper is to improve the intelligence level of assembly systems through the development and application of Internet of Things technology. Take the mechanical product assembly system as the research object, the research methods are as follows: First, on the basis of analyzing the connotation and characteristics of manufacturing associations, proposed the manufacturing environment of the Internet of Things. Then, constructed the overall operation framework, network physical environment and topology structure are of the system, and refined the key technologies of system modeling. Object-oriented Petri nets and VisObjNet simulation techniques are used for abstract modeling and simulation systems. Finally, developed the prototype of the intelligent assembly system of mechanical products under the Internet of Things background, and further verified the feasibility and effectiveness of the method proposed in this paper. The research results show that the prediction accuracy of the online monitoring system can reach 98.8% by collecting acceleration and noise in real time. The efficient and green processing parameter optimization system greatly optimizes the original processing efficiency. The method proposed in this paper effectively improves the transparency of the mechanical product assembly process. It provides a reference model and implementation path for building a smart assembly system for mechanical products under the IoT environment.

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“…Sensors from across the shop-floor can be used in conjunction with an IT/IS service to provide critical information about the processes in the white goods industry [61]. RFID can be used to track assembly execution and then to derive guidelines for smart assembly line development [62] and web-based systems (saas) to control smart internal logistics using mobile robots [68]. Motion Analysis System (MAS) to monitor and evaluate manual production processes [63,93].…”

Section: Industry 40 Technologies For Improving Processes and Decisionsmentioning

confidence: 99%

Lean Manual Assembly 4.0: A Systematic Review

2020

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In a demand context of mass customization, shifting towards the mass personalization of products, assembly operations face the trade-off between highly productive automated systems and flexible manual operators. Novel digital technologies—conceptualized as Industry 4.0—suggest the possibility of simultaneously achieving superior productivity and flexibility. This article aims to address how Industry 4.0 technologies could improve the productivity, flexibility and quality of assembly operations. A systematic literature review was carried out, including 234 peer-reviewed articles from 2010–2020. As a result, the analysis was structured addressing four sets of research questions regarding (1) assembly for mass customization; (2) Industry 4.0 and performance evaluation; (3) Lean production as a starting point for smart factories, and (4) the implications of Industry 4.0 for people in assembly operations. It was found that mass customization brings great complexity that needs to be addressed at different levels from a holistic point of view; that Industry 4.0 offers powerful tools to achieve superior productivity and flexibility in assembly; that Lean is a great starting point for implementing such changes; and that people need to be considered central to Assembly 4.0. Developing methodologies for implementing Industry 4.0 to achieve specific business goals remains an open research topic.

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Performance analysis of the RFID system for optimal design of the intelligent assembly line in the learning factory

Cited by 20 publications

References 11 publications

Using RFID and Industrial IoT in Manual Assembly for Data Collection and Processing in Learning Factories

Using RFID and Industrial IoT in Manual Assembly for Data Collection and Processing in Learning Factories

Modeling and Key Technologies of Intelligent Assembly System for Mechanical Products

Lean Manual Assembly 4.0: A Systematic Review

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