Preparation and Mechanical Property of Tantalum Alloying Layer on Ti6Al4V Alloy
Bibliographic record
Abstract
[Purposes] In order to improve the surface performance of Ti6Al4V (TC4) alloy, a double glow plasma surface alloying technology is used to prepare a tantalum alloying layer on its surface. Combined with Taguchi experimental design, the effect of process parameters on surface hardness of tantalum alloying layer is studied. [Methods] The characteristics of tantalum alloying layer were analyzed by optical microscopy, X-ray diffraction, scanning electron microscopy, and energy dispersive spectrometer. The mechanical properties of TC4 substrate and tantalum alloying layer were compared by using microhardness tester and nanoindentation instrument. [Results] The results show that the optimized process parameters include temperature of 750 ℃, source-cathode voltage difference of 350 V, and holding time of 2 h. The tantalum alloying layer obtained under the optimal process parameters is continuous, uniform, and compact, consisting mainly of α-Ta and intermetallic compounds. The surface hardness of the tantalum alloying layer is increased by about 3.2 times compared with that of the TC4 substrate. While the H/E and H3/E2 values were 2.1 times and 15.0 times those of the TC4 substrate, respectively. Therefore, double glow plasma surface alloying treatment with tantalum diffusion has significantly improved the resistance of TC4 alloy to local plastic deformation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".