A Review on Abrasive Wear of Aluminum Composites: Mechanisms and Influencing Factors
Bibliographic record
Abstract
Aluminum matrix composites (AMCs) find extensive use across diverse industries such as automotive, aerospace, marine, and electronics, owing to their remarkable strength-to-weight ratio, corrosion resistance, and mechanical properties. However, their limited wear resistance poses a challenge for applications requiring high tribological performance. Abrasive wear emerges as the predominant form of wear encountered by AMCs in various industrial settings, prompting significant research efforts aimed at enhancing their wear resistance. Over the past decades, extensive research has investigated the influence of various reinforcements on the abrasive wear behavior of AMCs. This paper presents a comprehensive review of the impact of different variables on the wear and tribological response of aluminum composites. This review explores possible wear mechanisms across various tribosystems, providing examples drawn from the analysis of existing literature. Through detailed discussions on the effects of each variable, conclusions are drawn to offer insights into optimizing the wear performance of AMCs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".