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A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing
Journal of Manufacturing Systems ( IF 12.2 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.jmsy.2020.11.012
Pei Wang , Ming Luo

Abstract Digital twin takes Industrial Internet as a carrier deeply coordinating and integrating virtual spaces with physical spaces, which effectively promotes smart factory development. Digital twin-based big data learning and analysis (BDLA) deepens virtual and real fusion, interaction and closed-loop iterative optimization in smart factories. This paper proposes a digital twin-based big data virtual and real fusion (DT-BDVRL) reference framework supported by Industrial Internet towards smart manufacturing. The reference framework is synthetically designed from three perspectives. The first one is an overall framework of DT-BDVRL supported by Industrial Internet. The second one is the establishment method and flow of BDLA models based on digital twin. The final one is digital thread of DT-BDVRL in virtual and real fusion analysis, iteration and closed-loop feedback in product full life cycle processes. For different virtual scenes, iterative optimization and verification methods and processes of BDLA models in virtual spaces are established. Moreover, the BDLA results can drive digital twin running in virtual spaces. By this, the BDLA results can be validated iteratively multiple times in virtual spaces. At same time, the BDLA results that run in virtual spaces are synchronized and executed in physical spaces through Industrial Internet platforms, effectively improving the physical execution effect of BDLA models. Finally, the above contents were applied and verified in the actual production case study of power switchgear equipment.

中文翻译:

工业互联网支持的基于数字孪生的大数据虚实融合学习参考框架迈向智能制造

摘要 数字孪生以工业互联网为载体,将虚拟空间与物理空间深度协调融合,有效推动了智能工厂的发展。基于数字孪生的大数据学习与分析(BDLA)深化智能工厂中虚实融合、交互和闭环迭代优化。本文提出了一种工业互联网支持的基于数字孪生的大数据虚实融合(DT-BDVRL)参考框架,面向智能制造。参考框架是从三个角度综合设计的。第一个是工业互联网支持的DT-BDVRL整体框架。二是基于数字孪生的BDLA模型的建立方法和流程。最后一个是DT-BDVRL在虚实融合分析中的数字线程,产品全生命周期过程中的迭代和闭环反馈。针对不同的虚拟场景,建立了虚拟空间中BDLA模型的迭代优化和验证方法和流程。此外,BDLA 结果可以驱动数字孪生在虚拟空间中运行。通过这种方式,可以在虚拟空间中多次迭代验证 BDLA 结果。同时,运行在虚拟空间的BDLA结果通过工业互联网平台在物理空间同步执行,有效提升BDLA模型的物理执行效果。最后,将以上内容在电力开关设备实际生产案例研究中进行了应用和验证。建立了虚拟空间中BDLA模型的迭代优化和验证方法和流程。此外,BDLA 结果可以驱动数字孪生在虚拟空间中运行。通过这种方式,可以在虚拟空间中多次迭代验证 BDLA 结果。同时,运行在虚拟空间的BDLA结果通过工业互联网平台在物理空间同步执行,有效提升BDLA模型的物理执行效果。最后,将以上内容在电力开关设备实际生产案例研究中进行了应用和验证。建立了虚拟空间中BDLA模型的迭代优化和验证方法和流程。此外,BDLA 结果可以驱动数字孪生在虚拟空间中运行。通过这种方式,可以在虚拟空间中多次迭代验证 BDLA 结果。同时,运行在虚拟空间的BDLA结果通过工业互联网平台在物理空间同步执行,有效提升BDLA模型的物理执行效果。最后,将以上内容在电力开关设备实际生产案例研究中进行了应用和验证。运行在虚拟空间的BDLA结果通过工业互联网平台在物理空间同步执行,有效提升BDLA模型的物理执行效果。最后,将以上内容在电力开关设备实际生产案例研究中进行了应用和验证。运行在虚拟空间的BDLA结果通过工业互联网平台在物理空间同步执行,有效提升BDLA模型的物理执行效果。最后,将以上内容在电力开关设备实际生产案例研究中进行了应用和验证。
更新日期:2021-01-01
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