The effect of microstructure evolution on the wear behavior of tantalum processed by Indirect Extrusion Angular Pressing
Bibliographic record
Abstract
This article studies the evolution of microstructure and the wear resistance in tantalum processed by a newly developed Severe Plastic Deformation (SPD) technique called Indirect Extrusion Angular Pressing (IEAP). The microstructure and tribological behavior of nanostructured tantalum processed by IEAP were analyzed in this work. The samples were extruded for two, five, and twelve passes of IEAP and then exposed to ball-on-disk wear testing in dry sliding conditions. It was shown that after twelve IEAP passes, an extensive grain refinement down to 500 nm was achieved, hardness increased, and a high dislocation density formed in the material. The wear resistance of the material improved successively after each pass of IEAP, and the wear rate decreased, although the friction coefficient did not change. Evaluation of the morphology of the wear tracks showed that the dominant wear mechanisms were comprised of galling, adhesive wear, pitting and microplowing. Refinement of the microstructure by IEAP led to a reduction in adhesive wear and pitting while a slight increase in oxidation appeared. Comparison of the results of wear testing between tantalum against steel balls and tantalum against alumina balls showed that the presence of alumina generated a larger portion of adhesive wear, making the wear mechanism more complicated while the tantalum-steel pair presented milder wear.
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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 | 0.000 | 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) | 0.001 | 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".