Evaluation of Low Cost Titanium Alloy Products
Bibliographic record
Abstract
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. Lockheed Martin has initiated “Project Black Ti” to reduce the cost of titanium parts by reducing the titanium consumption but not the quantity of titanium parts. Ultimately, we want to reduce the inherent waste in the current processing of titanium alloy products. The Kroll process, by which most titanium product is made today, is nearly 60 years old. Kroll himself predicted the process would be replaced within 15 years due to inherent inefficiencies – in 1950. Titanium is also mis-characterized as a precious metal, which it is not. It is the ninth most abundant element on the earth’s surface. Aluminum by comparison is the third most abundant but has a much more efficient method to convert it to a usable form. Until the turn of the 20th century, aluminum was considered to be as precious as platinum until the Bayer Process brought prices down from $1200/kg to $0.60/kg. Regarding titanium, one way to improve efficiency and buy less material to make the same parts is via Powder Metallurgy (PM). Until recently, titanium alloy powder was very expensive. However, new methods of producing titanium alloy have been developed which generate powder as an output versus massive ingots, which require multiple melts to achieve homogeneity. With powder, in theory, we should be able to get much closer to net shape and reduce the initial buy and reduce significant machining costs. These low cost titanium powders are becoming commercially available, which has the potential to initiate a paradigm shift in the applications of titanium. PM technologies and the consolidation of these new powders are now economically viable with the potential cost of the new powders running approximately an order of magnitude less than conventional PM grade powders. This paper will present the current status of “Project Black Ti” and its potential impact to the F-35 program.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".