On Vacuum Plasma Spray Forming of Ti-6Al-4V
Bibliographic record
Abstract
Abstract The growing need for new materials and material combinations with superior properties for severe service applications has led to the development of near net-shape forming techniques for certain materials, such as superalloys, refractory metals and highly reactive metals, including Titanium and its alloys. In this work, the vacuum plasma spraying process (VPS) was optimized to prepare dense spray-formed components from high purity plasma atomized Ti-6Al-4V powder. VPS offers a unique environment for spraying reactive materials such as Ti- 6Al-4V as a controlled inert atmosphere is used during deposition of the material. Three particle size distributions of the powder were used to investigate the influence of the starting powder characteristics (size, chemical composition) on the resulting spray-formed material. Post-deposition heat-treatments were subsequently applied to modify the deposit structure in order to improve their mechanical properties. The as-deposited and post-treated specimens were characterized for their internal microstructure and mechanical properties. Results indicate that a combination of high purity starting powder, controlled environment, and tailored deposition and post-processing conditions can be used to produce dense spray-formed Ti-6Al-4V structures with properties comparable to those of cast and wrought materials. Yield strength in the order of 800 MPa, with ultimate tensile strength close to 900 MPa and elongation near 10% were measured for spray-formed and heat-treated Ti-6Al-4V specimens. The results of this investigation on vacuum plasma spray forming of Ti-6Al-4V are presented in a series of two papers. The first one (this one) focuses on the preparation of the spray-form components, and on the resulting mechanical characteristics. The second paper is dedicated to the detailed characterization of the internal microstructure of the as-sprayed and heat-treated deposits, and the correlation with the measured mechanical properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".