Impact of Cleaning Efficiency on Disc Cutter Drilling Performance
Bibliographic record
Abstract
Large diameter drilling operations, including tunnel boring and raise boring, are capital-intensive projects. As such, proper estimation of time and cost is critical to the planning of the drilling project. To arrive at the correct estimation of the drilling time during the drilling phase, accurate prediction of the drilling performance is needed. In large diameter applications, disc cutters are the primary cutting tools, hence, several investigations have focused on developing accurate estimation of disc cutter forces. Other studies have also sought to understand the impact of rotary speed and cutter geometry on drilling performance. This study seeks to contribute to existing body of knowledge by evaluating the impact of cuttings cleaning efficiency on disc cutter drilling performance. This technical paper presents the results of two sets of drilling experiments. Both experiments were conducted under atmospheric condition on the same granite block using same rotary cutting machine and tri-disc disc cutter with tungsten carbide inserts. Same drilling parameters were applied during each of these experiments. However, the difference lay in the adopted cuttings evacuation method. One drilling procedure adopted the dry method wherein the cuttings were evacuated with vacuum while in the second procedure, the cuttings were cleaned using the jetting action of a high spray nozzle. The results of these experiments show how much influence the cleaning efficiency has on the disc cutter drilling performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".