Evaluation of TBM Cutter Wear in Granite and Developing a Cutter Life Prediction Model for Face Cutters Based on Field Data: A Case Study
Abstract
:1. Introduction
2. Project and Machine Overview
2.1. Project Description
2.2. Machine Specifications and Cutter Arrangement
3. Cutter Wear in Granite
3.1. Analysis of Accumulated Wear Extent and Replacement Number of Cutter Rings with Different Installation Radii
3.2. Analysis of Wear Extent for Different Types of Disc Cutters
3.3. Analysis of Cutter Ring Replacements for Different Types of Disc Cutters
3.4. Analysis of Average Utilization Rate of Different Types of Disc Cutters
4. Developing a Cutter Life Prediction Model for Face Cutters
4.1. Selecting a Cutter Life Evaluation Index
4.2. Sensitivity Analysis
4.2.1. Grey Relational Analysis
4.2.2. Sensitivity Analysis of Cutter Ring Wear Rate to Intact Rock Parameters and TBM Tunneling Parameters
4.3. Development of a New Cutter Life Prediction Model
5. Conclusions
- (1)
- With increases in the installation radius, the accumulated wear extent showed a linearly increasing trend for both the center and the face cutter, while it increased first and then decreased for the gauge cutter, and the accumulated replacement number showed a linear growth trend for the face cutter.
- (2)
- The accumulated wear extent of the average single cutter position of the gauge cutter was about three times that of the face cutter and seven times that of the center cutter.
- (3)
- The numbers of replaced cutter rings for the average single cutter position of the center cutter and face cutter were comparable, and the number of replaced cutter rings for the average single cutter position of the gauge cutter was about 3–4 times that of the center cutter and face cutter.
- (4)
- The average utilization rate of the gauge cutter was the highest (80.97%), followed by the face cutter (44.20%), and that of the center cutter was the lowest (only 30.82%).
- (5)
- The sensitivity of the cutter ring wear rate of the face cutter to the intact rock parameters was in the following order: UCS, CAI, EQC, Q, and RAI, and that to the TBM tunneling parameters was in the following order: F, RPM, PR, T, and PRev. A prediction model (R2 = 0.964) for the cutter ring wear rate of the face cutter based on F, UCS, and RPM was established through multiple regression analysis.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Technical Parameter | Design Value |
---|---|
TBM model | Robbins MB280 (Robbins Co., Solon, OH, USA) |
TBM diameter (m) | 8.5 |
Cutterhead power (kW) | 3300 |
Cutterhead nominal torque (kN·m) | 6713 |
Cutterhead nominal thrust (kN) | 16,509.5 |
Maximum allowable thrust (kN) | 20,491 at 345 bar |
Rotational speed (rpm) | 0–6.9 |
Thrust cylinder stroke (mm) | 1829 |
Conveyor capacity (t/h) | 2196 |
Cutter Number | 1–8 | 9–43 | 44–46 | 47–51 | 52–53 |
---|---|---|---|---|---|
Allowable wear limit (mm) | 25 | 35 | 25 | 19 | 12 |
Date: | Mileage: | Maintenance Start Time: | Maintenance End Time: | Recorder: | ||
---|---|---|---|---|---|---|
Cutter Position № | Disc Cutter Number | Reason | Wear Extent (mm) | |||
Detachment | Installation | Detachment | Installation | |||
1 | ||||||
3 | ||||||
2 | ||||||
4 | ||||||
5 | ||||||
7 | ||||||
6 | ||||||
8 | ||||||
9 | ||||||
10 | ||||||
… | ||||||
… | ||||||
52 | ||||||
53 |
№ | Chainage (m) | Intact Rock Parameters | TBM Tunneling Parameters | K (mm/m) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
UCS (MPa) | Q (%) | EQC (%) | CAI | RAI | PR (m/h) | F (kN) | T (kN·m) | RPM (r/min) | PRev (mm/r) | |||
1 | 2581 | 95 | 27 | 44 | 4.2 | 43 | 2.50 | 17,915 | 2600 | 5.92 | 7.01 | 1.23374 × 10−5 |
2 | 2923 | 90 | 19 | 43 | 4.0 | 38 | 2.47 | 18,138 | 2470 | 5.87 | 7.00 | 1.31062 × 10−5 |
3 | 4272 | 65 | 19 | 39 | 3.8 | 25 | 1.87 | 18,115 | 1969 | 6.19 | 5.01 | 9.31849 × 10−6 |
4 | 4491 | 55 | 12 | 27 | 4.0 | 15 | 3.31 | 18,437 | 2525 | 5.73 | 7.70 | 1.19706 × 10−5 |
5 | 5283 | 65 | 17 | 37 | 4.3 | 25 | 2.94 | 18,768 | 2839 | 6.36 | 7.63 | 1.01884 × 10−5 |
6 | 5828 | 85 | 24 | 39 | 4.3 | 33 | 2.23 | 19,219 | 2348 | 6.15 | 6.04 | 1.34693 × 10−5 |
7 | 6740 | 35 | 12 | 27 | 2.9 | 10 | 3.11 | 16,916 | 2821 | 6.20 | 8.35 | 5.69578 × 10−6 |
8 | 7298 | 80 | 14 | 35 | 4.0 | 29 | 2.69 | 18,807 | 2704 | 6.20 | 6.74 | 1.27790 × 10−5 |
9 | 7588 | 65 | 30 | 49 | 4.5 | 32 | 2.81 | 17,988 | 5517 | 6.14 | 7.50 | 1.01972 × 10−5 |
10 | 8530 | 40 | 24 | 28 | 1.8 | 12 | 2.93 | 16,647 | 2576 | 6.08 | 7.96 | 4.27676 × 10−6 |
11 | 8592 | 45 | 9 | 22 | 2.6 | 10 | 2.65 | 15,774 | 2545 | 6.03 | 9.10 | 4.23748 × 10−6 |
12 | 10,191 | 75 | 13 | 32 | 4.0 | 24 | 2.68 | 17,884 | 2810 | 6.08 | 7.28 | 8.93241 × 10−6 |
Unstandardized Coefficients | Standardized Coefficients | t | p | Collinearity Diagnosis | |||
---|---|---|---|---|---|---|---|
B | SE | Beta | VIF | Tolerance | |||
Constant | −6.620 × 10−6 | 0.000 | −0.783 | 0.456 | |||
UCS (MPa) | 6.097 × 10−8 | 0.000 | 0.357 | 3.703 | 0.006 | 2.057 | 0.486 |
F (kN) | 2.304 × 10−9 | 0.000 | 0.680 | 7.105 | 0.000 | 2.032 | 0.492 |
RPM (r/min) | −4.758 × 10−6 | 0.000 | −0.242 | −3.381 | 0.010 | 1.136 | 0.880 |
R squared | 0.964 | ||||||
Adjusted R squared | 0.950 | ||||||
F-test | F (3,8) = 71.041, p = 0.000 | ||||||
D-W | 2.047 |
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Liu, J.; He, T.; Peng, X.; Pan, Y. Evaluation of TBM Cutter Wear in Granite and Developing a Cutter Life Prediction Model for Face Cutters Based on Field Data: A Case Study. Buildings 2024, 14, 2453. https://doi.org/10.3390/buildings14082453
Liu J, He T, Peng X, Pan Y. Evaluation of TBM Cutter Wear in Granite and Developing a Cutter Life Prediction Model for Face Cutters Based on Field Data: A Case Study. Buildings. 2024; 14(8):2453. https://doi.org/10.3390/buildings14082453
Chicago/Turabian StyleLiu, Jianping, Tiankui He, Xingxin Peng, and Yucong Pan. 2024. "Evaluation of TBM Cutter Wear in Granite and Developing a Cutter Life Prediction Model for Face Cutters Based on Field Data: A Case Study" Buildings 14, no. 8: 2453. https://doi.org/10.3390/buildings14082453