Method of tasks and resources matching and analysis for cyber-physical production system

Jiang, Zhenan; Jian-hui, Yang; Mingcheng, E; Li, Qi

doi:10.1177/1687814018777828

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…There has been a small but growing volume of studies [51][52][53][54]58,68,[75][76][77][128][129][130][131][132]152,153] claiming that industrial enterprises advance as wireless sensor networks to constantly control the operations of their plants. Internet of Things-based real-time production logistics, product decision-making information systems, and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors.…”

Section: Discussionmentioning

confidence: 99%

“…Smart manufacturing harnesses predictive production systems systematically [127][128][129][130][131][132]: cognitive networked assets can predict, identify cause, and redesign malfunctioning events automatically. CPPS-related data are inspected and networked between a physical industrial unit and the cyber computational space, integrating smart analytics to grasp undetectable issues for swift and precise decision-making.…”

Section: Deep Learning-assisted Smart Process Planning Internet Of Things-based Real-time Production Logistics and Sustainable Industrialmentioning

confidence: 99%

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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

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Section: Discussionmentioning

confidence: 99%

Section: Deep Learning-assisted Smart Process Planning Internet Of Things-based Real-time Production Logistics and Sustainable Industrialmentioning

confidence: 99%

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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“…The infant failure probabilities of root nodes (components) are simply the inherent infant failure probabilities analyzed in equation (3). The conditional probability tables (CPTs) represent failure dependency relationships of these nodes.…”

Section: Analysis Of Dependent Infant Failure Risk Considering Functional Failure Dependencymentioning

confidence: 99%

“…However, most of manufacturers lack sufficient knowledge of product infant failure mechanism and quantitative infant failure risk modeling technology. 3 These issues result in the weakness of warranty policy in preventing and controlling infant failure risk. For example, if a proactive infant failure risk analysis for Samsung Galaxy Note 7 exists, 4 then the painful damage due to battery explosion may be remarkably reduced or avoided.…”

Section: Introductionmentioning

confidence: 99%

Product infant failure risk modeling based on quality variation propagation and functional failure dependency

Chen

Liu

et al. 2018

Advances in Mechanical Engineering

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Product infant failure is the most crucial part of product quality risk that critically affects customer satisfaction. However, most of manufacturers lack sufficient understanding of the formation mechanism of product infant failure and quantitative infant failure risk modeling technology. These issues cause the inefficiency of warranty policy in preventing and controlling infant failure risk. Therefore, first, on the basis of the reverse mapping process of axiomatic design, an infant failure risk formation chain, that is, “process quality variation—physical defect—functional vulnerability—infant failure,” is proposed in this study by considering quality variation propagation and functional failure dependency to determine the inherent formation and dependent enlarging process of the risk. Second, on the basis of the risk formation chain, the infant failure risk is modeled from inherent risk and dependent risk. Specifically, the inherent infant failure risk is computed based on the stream of quality variation in production, whereas the dependent infant failure risk is computed by a Bayesian network by considering the functional failure dependency. Finally, a case study of an illustrative electromechanical system is introduced to verify the applicability of the proposed method. Result shows that the proposed method has a better performance in infant failure risk modeling.

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“…The research is like Han et al, 3 a good example of the complexity faced in scheduling large projects and potential solutions to these issues. Jiang et al 5 propose a method for resource and task matching in cyber-physical production systems, where the challenge is the nature of the tasks compared to the capabilities of the manufacturing resources. The methods are based on Petri-nets and neural networks and provide a reliable approach to solve this issue.…”

Section: Intelligent Manufacturing/production Systems: Modeling Algorithms and Optimizationmentioning

confidence: 99%

Intelligent manufacturing/production systems: Modeling, algorithms, and optimization

Nielsen

Banaszak²,

Bocewicz³

et al. 2018

Advances in Mechanical Engineering

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Method of tasks and resources matching and analysis for cyber-physical production system

Cited by 6 publications

References 15 publications

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Product infant failure risk modeling based on quality variation propagation and functional failure dependency

Intelligent manufacturing/production systems: Modeling, algorithms, and optimization

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