Modelling and assessment of carbon fiber reinforced aluminum matrix composites and their laminate squeeze casting fabrication
Bibliographic record
Abstract
Abstract The equibiaxial bend behavior of laminate carbon fiber fabric reinforced aluminum matrix composites is modelled and assessed. Analytical modelling and finite element analysis are comparatively investigated to study the mechanical properties, with particular focus on the elastic modulus and flexural strength. The investigation allows evaluating how far the experimental results deviate from idealized assumptions of the models, which provides insight into the composite quality and the effectiveness of the used laminate squeeze casting technique. Specifically, discrepancies shed light on the interlaminate and fiber-matrix interface bond as well as on the stability of the laminate layers during fabrication. The two model approaches are in good agreement with differences below 8%. Moreover, the models agree with experimental data in predicting an overall improvement in properties with increasing carbon fiber content up to 4.89 vol%. Overall, the composite samples outperform the model predictions, which indicates good interface bonding. However, microstructure investigations also indicate that the outperformance is partly caused by a shifting of the carbon fibers during squeeze casting closer to the later bend tensile loaded surface due to their lower density compared to aluminum. The result is higher load bearing capacity of the composites than estimated by the models that assume perfectly symmetrical composite structure. The experimental outperformance in ultimate flexural strength vanishes at higher carbon fiber contents. This is due to imperfect interlaminate and fiber-matrix interfaces where some defects such as pores, carbides and oxide particles tend to locate, leading to damage initiation and potentially interface failure.
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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 | 0.001 | 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".