Influence of Cutting Conditions on the Wear Resistance of Tools with a TiB2 Coating during Titanium Alloy Machining
Bibliographic record
Abstract
Abstract— The effect of cutting conditions on the tribotechnical characteristics and wear resistance of cutting tools with and without TiB2 coating during processing of a TiAl6V4 alloyed titanium alloy has been investigated. It was found that when processing TiAl6V4 alloy, the efficiency of a TiB2 coating on carbide cutting tools significantly depends on the cutting conditions. Wear estimates in combination with XPS and SEM studies of worn surfaces show that TiB2 coated tools are most efficiently used for rough turning at low cutting speeds (45 m/min) and a large depth of cut (2 mm) under conditions of intense build-up. It is assumed that this is due to the formation of thermal barrier films of TiC, as well as a large amount of tribooxide B2O3, which serves as a liquid lubricant. During finishing (the finishing operation) at higher cutting speeds (80 and 150 m/min), when crater wear on the front surface of the cutter prevails, the wear resistance of the coated and uncoated tools is practically the same. This indicates that there is no one-size-fits-all solution for different machining conditions of alloyed titanium alloy when different wear mechanisms dominate.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".