Laminate Squeeze Casting and Equibiaxial Bend Behavior of Carbon Fiber Fabric Reinforced Aluminum Matrix Composites
Bibliographic record
Abstract
The 2D bend behavior of composites fabricated by the new laminate squeeze casting technique is studied. Particular focus is on effects of the initial alternating solid‐state laminate configuration of aluminum 6061 sheets and carbon fiber fabric. Equibiaxial pin‐on‐ring bend tests are performed. It is shown that hydrostatic pressures around 40 MPa are required, which is at the higher end of the range reported for conventional squeeze casting. This is primarily due to the oxide scale on the aluminum layers that can hinder infiltration and weaken the fiber–matrix interface. However, above the threshold squeeze pressure, flexural modulus, yield strength, and strain hardening modulus continuously increase by up to 11.65%, 90.12%, and 248.28%, respectively. The strong strain hardening behavior suggests initial plastic deformation as pre‐condition for full load transfer to the fibers, indicating that the composites are most efficient when pre‐deformed following casting. Moreover, the ultimate flexural strength improves by up to 322.23% at 3.74 vol% with a maximum value of 1050.95 MPa. The major property improvements are primarily due to the possibility offered by the laminate technique to selectively place the reinforcement at critical sections, in this case, close to the composite surfaces with the highest bend stresses.
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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 | 0.000 | 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".