Tool wear in disk milling grooving of titanium alloy
Bibliographic record
Abstract
The work efficiency of grooving machining can be improved by the approach of disk milling. However, the problem of tool wear was serious because of big milling force and high milling temperature in manufacture process, by which the tool life and the machined surface quality were influenced. In this study, disk-milling grooving experiments of titanium alloy were designed and conducted. First, milling force and milling temperature were examined, which provided theory basis to tool wear. Then, the tool life, wear patterns, and its corresponding mechanisms were investigated in detail through scanning electron microscope observation, X-ray energy-dispersive spectrometer, and automatic tool analyzer. By analyzing experimental results, it was found that, for an example of five-stage compressor blisk of a certain type aircraft engine, disk cutter can only remove about 50 tunnels’ volume after five times grinding before wear-out failure, the tool life still needs to be greatly enhanced. The damage morphologies were delamination, thermal fatigue crack, plastic deformation, and tipping. Wear mechanisms were the synergistic interaction among adhesion wear, oxidation wear, and diffusion wear. Thermal crack and tipping were easily found for the cutting edges around the keyway. The oxidation degree of major cutting edge was higher than minor cutting edge; rake face was severe compared to flank face. The element W easily diffused into the titanium alloy, the diffusion ability of Co and C were weaker than W, and the element Ti was dead in diffusion process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".