Digital Twin Modeling for Demand Responsive Transit

Author(s):  
Sen Deng ◽  
Jiaming Zhong ◽  
Shengmei Chen ◽  
Zhaocheng He
Author(s):  
Chunlong Wu ◽  
Youcheng Zhou ◽  
Marcus Vinicius Pereia Pessôa ◽  
Qingjin Peng ◽  
Runhua Tan

2021 ◽  
Author(s):  
Guang Ji ◽  
Jian-guo Hao ◽  
Jia-long Gao ◽  
Cheng-zhao Lu

Author(s):  
Qiangwei Bao ◽  
Gang Zhao ◽  
Yong Yu ◽  
Sheng Dai ◽  
Wei Wang

The digital twin concept, as a widely accepted philosophy for a new generation of digital manufacturing research, plays a significant role in the Industry 4.0 era. As the prerequisite for in-depth application of digital manufacturing in assembly, the collecting, modeling and utilizing approach for historical data of machining process and inspection of part appear to be important to provide data support for integral manufacturing scenarios. However, the modeling approach to part digital twin is not comprehensive due to the structural heterogeneity of data, which hampers the real-time simulation and adjustment in assembly process. Therefore, a method of part digital twin modeling oriented to assembly is proposed in this article. The designing information of a part is obtained from a three-dimensional model with the model-based definition approach, while machining features are pre-defined and identified. Moreover, the assembly constrain relationships in the assembly unit that the part participate in are obtained, on account of which deviation transfer analysis can be accomplished, and key assembly features are filtered to be the carrier of processing and inspection data. An assembly-oriented part digital twin framework is constructed to demonstrate the main components and dataflow in creating a digital twin with information filtering and subsequent management. In addition, a case study is illustrated to show the entire process of part digital twin modeling and proves the practicality and efficiency of the method proposed.


2021 ◽  
Vol 64 ◽  
pp. 898-906
Author(s):  
Gaoqiang Chen ◽  
Jialei Zhu ◽  
Yanhua Zhao ◽  
Yunfei Hao ◽  
Chengle Yang ◽  
...  

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