Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components
The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities...
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| Other Authors: | , , , , , , , |
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2024
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/3087 https://doi.org/10.1109/TCE.2023.3326047. |
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| _version_ | 1862980619016863744 |
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| author | Mourad, Nahia |
| author2 | A. Alsattar, Hassan Qahtan, Sarah Zaidan, A.A. Deveci, Muhammet Member, IEEE Kumar Sangaiah, Arun Pedrycz, Witold Fellow, Life |
| author2_role | author author author author author author author author |
| author_facet | Mourad, Nahia A. Alsattar, Hassan Qahtan, Sarah Zaidan, A.A. Deveci, Muhammet Member, IEEE Kumar Sangaiah, Arun Pedrycz, Witold Fellow, Life |
| author_role | author |
| dc.creator.none.fl_str_mv | Mourad, Nahia A. Alsattar, Hassan Qahtan, Sarah Zaidan, A.A. Deveci, Muhammet Member, IEEE Kumar Sangaiah, Arun Member, IEEE Pedrycz, Witold Fellow, Life |
| dc.date.none.fl_str_mv | 2024-02-21 2025-05-21T16:31:32Z 2025-05-21T16:31:32Z |
| dc.identifier.none.fl_str_mv | Mourad, N. et al. (2024) “Decisioning-Based Approach for Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components,” IEEE Transactions on Consumer Electronics, 70(1). 0098-3063, 1558-4127 https://bspace.buid.ac.ae/handle/1234/3087 https://doi.org/10.1109/TCE.2023.3326047. |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | IEEE Transactions on Consumer Electronicsv70 n1 (202402): 3212-3221 |
| dc.subject.none.fl_str_mv | Cyber-physical metaverse manufacturing system components , control engineering tool , digital twin , multiple criteria decision-making |
| dc.title.none.fl_str_mv | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| dc.type.none.fl_str_mv | Article |
| description | The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human–Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue; formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS–FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS–FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. |
| id | budr_0bc06599ed0c901e0982367cfa76259f |
| identifier_str_mv | Mourad, N. et al. (2024) “Decisioning-Based Approach for Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components,” IEEE Transactions on Consumer Electronics, 70(1). 0098-3063, 1558-4127 |
| language_invalid_str_mv | en_US |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3087 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System ComponentsMourad, NahiaA. Alsattar, HassanQahtan, SarahZaidan, A.A.Deveci, MuhammetMember, IEEEKumar Sangaiah, ArunMember, IEEEPedrycz, WitoldFellow, LifeCyber-physical metaverse manufacturing system components , control engineering tool , digital twin , multiple criteria decision-makingThe optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human–Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue; formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS–FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS–FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS.IEEE2025-05-21T16:31:32Z2025-05-21T16:31:32Z2024-02-21ArticleMourad, N. et al. (2024) “Decisioning-Based Approach for Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components,” IEEE Transactions on Consumer Electronics, 70(1).0098-3063, 1558-4127https://bspace.buid.ac.ae/handle/1234/3087https://doi.org/10.1109/TCE.2023.3326047.en_USIEEE Transactions on Consumer Electronicsv70 n1 (202402): 3212-3221oai:bspace.buid.ac.ae:1234/30872026-01-29T16:47:29Z |
| spellingShingle | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components Mourad, Nahia Cyber-physical metaverse manufacturing system components , control engineering tool , digital twin , multiple criteria decision-making |
| title | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| title_full | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| title_fullStr | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| title_full_unstemmed | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| title_short | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| title_sort | Optimising Control Engineering Tools Using Digital Twin Capabilities and Other Cyber-physical Metaverse Manufacturing System Components |
| topic | Cyber-physical metaverse manufacturing system components , control engineering tool , digital twin , multiple criteria decision-making |
| url | https://bspace.buid.ac.ae/handle/1234/3087 https://doi.org/10.1109/TCE.2023.3326047. |