Influence of Platelet Boundary Irregularity on the Nonlinear Mechanical Behavior of Platelet-Reinforced Composites
Bibliographic record
Abstract
Defects, such as cell wall thickness variations, significantly influence cellular materials' mechanical characteristics. For instance, during a compression test in deformation mode, the crush bands are initiated at cells with a thinner wall thickness. This paper examines the effect of boundary irregularity on the nonlinear elastoplastic mechanical behavior of platelet-reinforced composite materials. Two types of stochastic (random distribution) platelets: with rounded corners and with sharp corners are generated using the Voronoi tessellation method. To better compare the effect of boundary irregularity, the composites are analyzed for three different volume fractions. The microstructure of the composite studied consists of isotropic and linear elastic platelets embedded in a perfectly plastic elastic matrix. Nonlinear numerical simulation of the tensile test was carried out using FE Zebulon software. The results show that the composites exhibit bilinear stress-strain behavior, consisting of two lines representing, respectively, the linear behavior (whose slope is Young's modulus) and the plastic behavior (whose slope is the strain hardening modulus). The effective Young's modulus, yield strength, and tangent modulus of the composites are calculated and compared in terms of the shape and volume fraction of the platelets. In the elastic zone, the results indicate that the curves of the two types of stochastic platelets overlap, giving very close results for Young's modulus. In the plastic zone, the effect of platelet irregularity is noticeable. It has been found that the platelets with rounded corners have a higher tangent modulus than those with sharp corners. This deference can improve the composite's ability to withstand more deformation under high loads, unlike platelets with sharp edges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".