Tailoring the microstructure and tribological properties in commercially pure aluminium processed by High Pressure Torsion Extrusion
Bibliographic record
Abstract
High Pressure Torsion Extrusion (HPTE) as a novel approach in mechanical nanostructuring of metallic materials and alloys has the potential to be utilized in industrial applications due to its unique features in fabricating bulk-nanostructured materials with enhanced mechanical and functional properties. Three different HPTE regimes based on the extrusion speed of the punch (v, mm/min) and rotational speed of the die (ω, rpm) were used in this work: v7w1, v1w1, and v1w3. The grain refinement obtained by this technique was outstanding since the initial grain size of 120 μm in annealed conditions was reduced to the final grain size of 0.7 μm in v1w3 in merely one pass of extrusion; however, each regime showed a different level of grain refinement depending on the imposed strain. Examination of the tribological properties by reciprocal wear testing in dry conditions revealed no significant change in the coefficient of friction; nevertheless, the mechanism of the wear from adhesion shifted to abrasion and the amount of displaced volume decreased. This modification is associated with the improvement of hardness and the reduction of plasticity in materials that confined the plastic shearing. Increasing the induced strain by changing the HPTE regimes decreased the overall displaced volume and reduced the built-up edge around the wear track.
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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".