Current Research and Prospect on Cutter Wear of Tunnelling Boring Machine

Huang Dan; Ma Hao; Yang Xiaocong; Li Yuxuan; Zheng Zhijie

doi:10.3969/j.issn.1007-2993.2023.03.001

Volume 37 Issue 3

Jun. 2023

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Article Navigation > GEOTECHNICAL ENGINEERING TECHNIQUE > 2023 > 37(3): 253-261

Huang Dan, Ma Hao, Yang Xiaocong, Li Yuxuan, Zheng Zhijie. Current Research and Prospect on Cutter Wear of Tunnelling Boring Machine[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2023, 37(3): 253-261. doi: 10.3969/j.issn.1007-2993.2023.03.001

Citation:

Huang Dan, Ma Hao, Yang Xiaocong, Li Yuxuan, Zheng Zhijie. Current Research and Prospect on Cutter Wear of Tunnelling Boring Machine[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2023, 37(3): 253-261. doi: 10.3969/j.issn.1007-2993.2023.03.001

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Current Research and Prospect on Cutter Wear of Tunnelling Boring Machine

doi: 10.3969/j.issn.1007-2993.2023.03.001

Huang Dan^{1, 2
,},
Ma Hao^{1, 2},
Yang Xiaocong^{1, 2},
Li Yuxuan^{1, 2},
Zheng Zhijie^{1, 2}

1.
BGRIMM Technology Group, Beijing 100160, China
2.
National Center for International Joint Research on Green Metal Mining, Beijing 102628, China

Received Date: 2022-01-04
Accepted Date: 2022-08-25
Rev Recd Date: 2022-03-16

Available Online: 2023-06-08

Publish Date: 2023-06-08

Abstract

Abstract

With the development of machinery manufacturing technology and economy, more and more project of water conservancy, transportation and mines use full face tunnel boring machine (TBM) to excavate tunnels. The wear of TBM cutter has great influences on the tunnelling efficiency and costs. The study on cutter wear is significant to improve the wear resistance of cutter and the timeliness of wear monitoring, reasonably predict the tunnelling period and cost, and take effective measures to shorten the construction period and reduce the project cost. The research of the types, influencing factors, monitoring methods and prediction models of cutter wear are reviewed. The direction for further study on the cutter wear is provided.
- TBM,
- cutter wear form,
- influencing factors of cutter wear,
- monitoring of cutter wear,
- prediction models of cutter wear

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References(45)

References

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