The Experimental Research of the Relationship Between Rock Surface Roughness and PDC Bit Wear
Bibliographic record
Abstract
By using rock grinding experiment machine to grinding different groups of rock, Using the electron microscope scanning technology to analysis of the wear profile amplification and Contour arithmetic mean deviation Ra, outline of the root mean square deviation Rq and average roughness parameter R the three surface roughness parameters to measure the new grinding surface roughness, using the PDC diamond compact for further PDC grinding experiments in different roughness of the generated sections. Before and after the grinding experiment calculate the lose weight of PDC diamond compact with electronic balance scales, through the analysis of experimental result data concluded that the degree of wear of PDC bit and the roughness grinding profile had a certain linear relationship in the grinding experiments, namely with the increase of roughness the trend of PDC bit wear was from reducing to increasing, and the wear volume reached its lowest point in a certain roughness parameter. using the scanning electron microscopy to analyze the section of PDC wear, and concluded that the wear mechanism of PDC is mainly abrasive wear ,accompanied by fatigue wear and adhesive wear, the wear process of PDC is the process of cleavage. The better way of diamond abrasion wear is: Diamond from exposing to micro broken to cleavage to fall off in the end. The worst way is diamond directly fall off.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".