Automated Deployment Scheme of Digital Twin Manufacturing System Based on Topology Optimization and Multiscale Modeling.
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| Title: | Automated Deployment Scheme of Digital Twin Manufacturing System Based on Topology Optimization and Multiscale Modeling. |
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| Authors: | Ji, Zhengbo1 (AUTHOR) 19831357@jscj.edu.cn, Fu, Xuelong1 (AUTHOR), Zhao, Linlin1 (AUTHOR), Sequenzia, Gaetano1 (AUTHOR) gsequenzia@dii.unict.it |
| Source: | Modelling & Simulation in Engineering. 6/12/2026, Vol. 2026, p1-16. 16p. |
| Subjects: | Digital twin, Multiscale modeling, Automation, Mathematical optimization, Resource allocation, Real-time computing, Multi-objective optimization |
| Abstract: | Aiming at the difficulties of topology reconstruction lag and multiobjective resource allocation conflict accumulation in the dynamic scenario of digital twin manufacturing system, the study constructs an automated deployment optimization model based on the synergy of topology optimization and multiscale modeling. Through the topology robustness extension and hierarchical reinforcement learning mechanism, the accuracy of real‐virtual mapping and the efficiency of cross‐level resource balancing are improved. The experimental results indicated that the model reached a hypervolume index of 0.97 in the simulation scenario, which was 18.3% higher than that of the traditional method. The value of solution set spacing was 0.05, and the distribution uniformity was optimized by 54.5%. In the dynamic topology reconfiguration test, the peak task conflict rate was 5.9%, the mean value of resource allocation was 80.7%, the standard deviation was 5.3%, and the degree of balance was 0.93. Actual production line verification revealed that the system throughput extreme value reached 1050 tasks/minute, and the average energy consumption per unit capacity was 89.7kWh. The deviation fluctuation range was ±11.4 kWh, which was 55.7% lower than that of the baseline scenario. The average resource utilization at device level was 86.1% (standard deviation 4.3%), and the cross‐tier load balancing degree was improved to 0.95. The extreme value of deployment time under high concurrency scenarios was stabilized at 8.7–14.5 ms. It verified its real‐time performance and robustness in dynamic heterogeneous environments. In summary, the proposed model can enhance the global adaptivity of virtual‐real collaboration in smart factories and provide solutions for dynamic resource allocation and multiobjective optimization. [ABSTRACT FROM AUTHOR] |
| Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194548050 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated Deployment Scheme of Digital Twin Manufacturing System Based on Topology Optimization and Multiscale Modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ji%2C+Zhengbo%22">Ji, Zhengbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 19831357@jscj.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fu%2C+Xuelong%22">Fu, Xuelong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Linlin%22">Zhao, Linlin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sequenzia%2C+Gaetano%22">Sequenzia, Gaetano</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gsequenzia@dii.unict.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Modelling+%26+Simulation+in+Engineering%22">Modelling & Simulation in Engineering</searchLink>. 6/12/2026, Vol. 2026, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Aiming at the difficulties of topology reconstruction lag and multiobjective resource allocation conflict accumulation in the dynamic scenario of digital twin manufacturing system, the study constructs an automated deployment optimization model based on the synergy of topology optimization and multiscale modeling. Through the topology robustness extension and hierarchical reinforcement learning mechanism, the accuracy of real‐virtual mapping and the efficiency of cross‐level resource balancing are improved. The experimental results indicated that the model reached a hypervolume index of 0.97 in the simulation scenario, which was 18.3% higher than that of the traditional method. The value of solution set spacing was 0.05, and the distribution uniformity was optimized by 54.5%. In the dynamic topology reconfiguration test, the peak task conflict rate was 5.9%, the mean value of resource allocation was 80.7%, the standard deviation was 5.3%, and the degree of balance was 0.93. Actual production line verification revealed that the system throughput extreme value reached 1050 tasks/minute, and the average energy consumption per unit capacity was 89.7kWh. The deviation fluctuation range was ±11.4 kWh, which was 55.7% lower than that of the baseline scenario. The average resource utilization at device level was 86.1% (standard deviation 4.3%), and the cross‐tier load balancing degree was improved to 0.95. The extreme value of deployment time under high concurrency scenarios was stabilized at 8.7–14.5 ms. It verified its real‐time performance and robustness in dynamic heterogeneous environments. In summary, the proposed model can enhance the global adaptivity of virtual‐real collaboration in smart factories and provide solutions for dynamic resource allocation and multiobjective optimization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/mse/4344492 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Digital twin Type: general – SubjectFull: Multiscale modeling Type: general – SubjectFull: Automation Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Multi-objective optimization Type: general Titles: – TitleFull: Automated Deployment Scheme of Digital Twin Manufacturing System Based on Topology Optimization and Multiscale Modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ji, Zhengbo – PersonEntity: Name: NameFull: Fu, Xuelong – PersonEntity: Name: NameFull: Zhao, Linlin – PersonEntity: Name: NameFull: Sequenzia, Gaetano IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 06 Text: 6/12/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16875591 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Modelling & Simulation in Engineering Type: main |
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