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Research Article
Wenjun Xu
Wuhan University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5370-3437
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This work is licensed under a CC BY 4.0 License. Read Full License
The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment is the key link in determining the optimal service composition to realize the value-added of ICRs resources. However, the traditional evaluation method ignores the positive and negative cooperative effects of the manufacturing capability correlation among the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and serious environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship to solve above problems. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which the evaluation indicators raw data is processed based on the service correlation model and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in assessment of sustainable manufacturing capability for ICRs.
Keywords
Industrial cloud robotics, Sustainable manufacturing capability, Correlation relationship, Dynamic assessment
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CURRENT STATUS: Under Review
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A new preprint design is coming!
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This preprint is under consideration at The International Journal of Advanced Manufacturing Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Wenjun Xu
Wuhan University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5370-3437
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 29 Mar, 2021
No community comments so far
Reviews received
Received 25 Mar, 2021
Editor assigned
On 01 Mar, 2021
First submitted
On 27 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment is the key link in determining the optimal service composition to realize the value-added of ICRs resources. However, the traditional evaluation method ignores the positive and negative cooperative effects of the manufacturing capability correlation among the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and serious environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship to solve above problems. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which the evaluation indicators raw data is processed based on the service correlation model and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in assessment of sustainable manufacturing capability for ICRs.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
The full text of this article is available to read as a PDF.
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