A Review of Research on Improving Wear Resistance of Titanium Alloys
Bibliographic record
Abstract
Titanium alloy is widely used as oil drill pipe material because of its light weight, high strength, good toughness, corrosion resistance, fatigue resistance, and good process performance. However, due to its low hardness, poor wear resistance, serious oxidation at high temperature (700 °C), and difficulty in lubrication, in oil and gas field exploration and development drilling, especially in deep wells, high displacement wells, horizontal wells, and highly deviated wells, wear and tear are prone to occur. The application and development of titanium alloys are greatly limited. This paper introduces the research status of the common surface modification technologies of titanium alloys, such as laser cladding, magnetron sputtering, plasma spraying, micro arc oxidation, etc. It points out the improvement effect of various modification technologies on the wear resistance and high-temperature oxidation resistance of titanium alloys and discusses the advantages and disadvantages of various modification technologies. A proposed method for enhancing the wear resistance and high-temperature oxidation resistance of titanium alloys was finally introduced, and its potential for future development was investigated.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".