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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 16875591 |
| DOI: | 10.1155/mse/4344492 |
