Experimental Study on Rock Breaking Effect of Axe-Shaped and Triangular-Ridged Cutters Under Impact Load
Bibliographic record
Abstract
In drilling engineering， optimized cutter selection for specific formations is critical to enhance rock breaking efficiency. To this end， a Split Hopkinson Pressure Bar （SHPB） apparatus was used to conduct impact-induced rock breaking tests of triangular-ridged and axe-shaped cutters under impact load. By means of analyzing rock breaking volume of cutters at different impact speeds using 3D scanning technology， the rock breaking effect of single cutter was evaluated. Then， the changes in dissipated energy of different types of cutters during rock breaking were analyzed， and the variation law of rock-breaking specific energy of cutter at different impact speeds were discussed. The study results show that the volume of impact crater increases with the increase of impact speed， and the volume of impact crater of axe-shaped cutter is smaller than that of triangular-ridged cutter. At the same speed， the fractal dimension and energy to fracture efficiency of axe-shaped cutter are greater than those of triangular-ridged cutter. At low impact speed， the rock-breaking specific energy of triangular-ridged cutter is smaller. At high impact speed， the rock breaking efficiency of axe-shaped cutter is higher. The study conclusions provide references for enhancing ROP and improving efficiency of PDC cutter in percussive drilling of hard rock formations.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".