High Speed Machining of Titanium Alloys
Bibliographic record
Abstract
Removal rates for machining titanium alloys are an order of magnitude slower than those for aluminum. The high strength and hardness coupled with the relatively low elastic modulus and poor thermal conductivity of titanium contribute to the slow speeds and feeds that are required to machine titanium with acceptable tool life. Titanium has extremely attractive properties for air vehicles ranging from excellent corrosion resistance to good compatibility with graphite reinforced composites and very good damage tolerance characteristics. At current Buy to Fly ratios, the F-35 Program will consume as much as seven million pounds of titanium a year at rate production. This figure is nearly double that of the F-22 Program, which has a much higher titanium content. As much as 50% of the final cost of titanium parts can be attributed to machining. Specifically, in this task, we are working to improve the material removal rate of titanium to reduce cost. Lockheed Martin is evaluating the potential to use lasers to heat the material ahead of the tool to reduce its strength. Coupled with other technologies that can improve the tool life and prevent the titanium material from welding to the tool, there is hope for a practical solution using similar milling machines to those which exist today, if not a simple retro-fit option. This presentation will present the current progress of this project and its potential impact to the Joint Strike Fighter.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.003 |
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".