Laboratory Study on the Efficiency of Disc Cutter Drilling With the Integration of a Vibration-Assisted Rotary Drilling (PVARD) Tool
Bibliographic record
Abstract
Abstract Efficient and economical mining of underground resources processes large-diameter drilling and tunnel boring machine (TBM) excavating operations. Optimizing drilling efficiency and lowering operational costs, resulting in the optimal fragmentation of the rock, the minimum consumption of energy, and increasing the cutter’s life all prove to be crucial. The vibration-assisted Rotary Drilling (pVARD) tool is a technology that was needed to overcome the challenges faced in drilling operations for mining operations. This study investigates the efficacy of (pVARD) in improving drilling performance by employing a single-edge disc cutter in drilling gabbro rock. This challenging and abrasive formation is typically encountered in tunnel boring and raise-boring operations. Conducted in a controlled laboratory setting utilizing a Laboratory Drilling Simulator (LDS), the study explored how pVARD technology, defined by controlled axial vibrations, affects drilling efficiency across various Weight on Bit (WOB) and constant Revolutions per Minute (RPM) inputs. Key performance metrics analyzed include Depth of cut (DOC), Torque, and groove, where pVARD demonstrates a significant elevation in ROP and a reduction in energy requirements compared to conventional drilling methods. Our findings revealed optimal pVARD configurations for specific drilling parameters in gabbro rock, demonstrating the technology’s potential to significantly improve drilling efficiency and mitigate vibration-related challenges commonly encountered in this rock type. The study provides valuable. Insights for applying pVARD in real-world drilling operations targeting gabbro formations. It sets the stage for further research into advanced pVARD configurations and their effects on wellbore stability in this challenging rock type.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".