Working mechanism and experimental study of split bit
Bibliographic record
Abstract
As a component directly acting on the rock in the drilling process, the low efficiency of rock breaking will occur when the bit is drilled. In this paper, a new type of split drill is designed in which the cutting ring can move relatively independently, as well as the kinematic analysis model of the cutting tooth is established. The displacement, velocity and acceleration values of the specific points on the cutting tooth ring of the bit are obtained, which verifies that the small cutting ring at the center of the split cone bit can improve the rock breaking efficiency at the center of the bit. Through the comparison experiments of different types of bits and split bits, the results show that the mechanical drilling rate of split single-tooth bits and triple-tooth bits increased by 11.7% and 12.4%, respectively, in Beipei limestone conditions. Meanwhile, the speed of those two increased by 8.9% and 11.4%, respectively, in Wusheng sandstone conditions, which effectively improved the rock-breaking performance. Combined with the results of bottom hole model, the characteristics of impact, invasion and cutting in the process of tooth rock breaking can be obtained, which effectively improves the rock breaking performance and verifies the rationality of bit design and the correctness of theoretical analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".