Disc cutter wear prediction based on the friction work principle
Bibliographic record
Abstract
As the main rock-breaking tool of the tunnel boring machine, wear of the disc cutter is affected by geological conditions, equipment factors, and tunneling parameters when it interacts with rock. Because of the complex factors affecting disc cutter wear, it is difficult to accurately predict the wear of the disc cutter. In this study, the rock-breaking mechanism and the force of the disc cutter were analyzed, and a theoretical prediction model of disc cutter wear was established based on the friction work principle. The parameters in the disc cutter wear prediction model were determined by simulation, and a prediction method of disc cutter wear is proposed. Finally, the wear prediction model of the disc cutter was verified by field wear data. The results show that the average error between the predicted value of the disc cutter and the actual wear data from the field is 16.1%. The wear prediction model of the disc cutter has high accuracy and adaptability. The research results provide an effective method for wear prediction of the disc cutter, which is of great significance and engineering value for cutter replacement and construction management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".