Effect of Surface Porosity on Tribological Properties of Sintered Pure Al and Al 6061
Bibliographic record
Abstract
Due to light weight, high specific strength, high corrosion resistance and good heat transfer ability, aluminium alloys are becoming attractive for critical structural applications. These alloys can be manufactured using powder metallurgy techniques in which porosity is a common characteristic. The presence of pores is responsible for decreasing effective load bearing cross sectional area and inducing stress concentration sites for strain localization and damage, decreasing both strength and ductility. The present study aims to establish a better understanding of the relationship between surface porosity and corresponding wear behavior. In this study, porous specimens were produced using powder metallurgy technique and the extent of wear damage and the type of wear was investigated under low load range of 1.5 - 5 N against AISI 52100 bearing steel ball using a reciprocating ball-on-flat configuration and frequency of 10 Hz. Scanning electron microscopy of the wear tracks and wear debris was carried out to understand wear mechanisms. This study revealed that due to combined effect of high stress intensity and subsurface cracking, wear rate increases with increasing porosity content. The friction and wear behavior of pure Al and Al 6061 as a function of porosity content can be attributed to their hardness and corresponding wear mechanism.
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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 | 0.001 |
| 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".