Microstructure, hardness, and tribological properties of AA2014 powder metallurgy alloys: A sizing mechanical surface treatment study
Bibliographic record
Abstract
• Sizing mechanical surface treatment significantly affects tribological performance. • Low-pressure sizing leads to high dislocation density around porosities, causing delamination. • Higher sizing pressure promotes uniform misorientation and improved wear resistance. • A uniform oxide tribolayer was observed in as-sintered samples. This study explores the influence of sizing mechanical surface treatment on the tribological response of AA2014 powder metallurgy (PM) alloy-steel tribosystem under reciprocating sliding wear. The impact of sizing pressure on wear mechanisms is analyzed using a combination of X-ray diffraction (XRD), electron backscatter diffraction (EBSD), surface topography, hardness testing, wear rate measurements, and microscopic analyses. The results show that sizing treatment can significantly alter wear mechanisms, shifting from abrasion and mild oxidative wear to delamination and cracking, especially at lower sizing pressures. Samples sized at 200 MPa and 300 MPa displayed pronounced delamination and cracking. In contrast, increasing the sizing pressure to 400 MPa enhanced mechanical properties, reduced the wear rate, and minimized delamination. This suggests that although sizing with relatively low sizing pressure can increase hardness, it may detrimentally affect the alloy’s wear performance by intensifying stress concentration effect. However, wear properties benefit from the superior mechanical properties gained through cold working of the alloy at a higher pressure of 400 MPa. This research highlights the critical role of sizing pressure in optimizing the tribological performance of sized aluminum PM alloys.
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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 | 0.001 |
| 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) | 0.002 | 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".