Characterization of surface damage in AlSi alloys
Bibliographic record
Abstract
Plastic deformation and damage accumulation at the contact surface are two important aspects of sliding wear of metal-matrix composite (MMC) materials such as AlSi alloys. The particular topography of the surfaces of the AlSi alloys has triggered the idea that the silicon particles form a load bearing surface over which the countersurfaces are sliding. Therefore, the wear resistance of AlSi surface is thought to originate from the high hardness of the silicon surface formed by the primary Si particles (inclusions). On the other hand, the mechanical strength of the reinforcement (Si)/matrix(Al) interface in a MMC is the primary factor determining the strength on the load bearing Si formation. In this work, the authors developed a novel method to characterize the interface strength of a MMC, combining a nano-/microindentation experiment and a finite element/atomistic analysis. The nano-/microindentation experiment was carried out by indenting individual reinforcement particles on a free surface with a nano-/microindenter. The dependence of indentation response on the interface properties was systematically studied, and the interface strength was extracted from the threshold stress for the sink in of the Si particles. With this method, the shear strength of an Al∕Si interface was measured approximately 330MPa, which compares well with the lower bound of an atomistic simulation with a modified embedded atom method potential [A. Noreyan et al., Acta Mater. 56, 3461 (2008)].
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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 | 0.000 |
| 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".