Development of Impregnated Diamond Bit with Primary and Secondary Abrasives Based on Matrix Weakening Theory
Bibliographic record
Abstract
Along with economic advancement, diamond bit is widely used in mine exploration and engineering geological investigation. To reduce the cost of impregnated diamond bit and improve its drilling efficiency in highly abrasive formation, this paper designs a diamond bit based on matrix weakening theory, and prepares the matrix from high-grade diamond abrasives and SiC particles. Through theoretical calculation, diamond bits were designed with six different formulas of diamond concentrations and weakened SiC particle concentrations. The theoretical analysis shows that the weakened SiC particle concentration fell within 0-50%; the number of diamond particles dropped from 750,115 to 375,150, saving the diamond cost by 50%. To further explore the life, drilling efficiency, and working mechanism of diamond bit, the apparent form of bit materials was analyzed through field tests, using EPMA-1720 electron probe microanalyzer and GENSIS60 energy spectrometer. The field tests show that: the proposed bit, with 35% of matrix skeleton and 65% of bonding metal, improved the drilling efficiency of ordinary impregnated diamond bit by 68%, while reducing the service life by merely 30%. The apparent form analysis shows that: In the diamond bit designed by matrix weakening theory, the weakened SiC particles could easily fall off the matrix surface, leaving recoverable pits on the surface. The non-smooth form weakens the abrasion resistance of the matrix, reduces the contact area between the crown of the drill bit and the rock surface at the hole bottom, and increases the pressure of the crown on the rock per unit area. The fallen wakened particles participate in the abrasion of the matrix at the hole bottom, improve the grinding ability of the rock powder at hole bottom, and promote the protrusion of new diamond particles in the matrix.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".