MICROSTRUCTURE BASED FINITE ELEMENT ANALYSIS OF PARTICLE FILLED POLYMER COMPOSITE
Bibliographic record
Abstract
Particle filled polymer composites are widely used because of its tailor-made properties and ease of manufacturability. Existing micro mechanical models to characterize heterogeneous material are based on the Representative Volume Element (RVE). The assumptions made in the RVE model, play a crucial role in the exact prediction of effective properties of the composites. In this work, microstructure based RVE is utilized to predict the effective properties of Solid Glass Microsphere (SGM) filled epoxy composite. The Scanning Electron Microscope (SEM) image obtained from the specimens fabricated at different loading fractions is processed in MATLAB. Canny edge detection algorithm is utilized for processing the images. The random dispersion of the particle is exactly modeled in ANSYS from the MATLAB output. The effective Young’s modulus of the SGM filled epoxy composite is determined. The numerically predicted values are compared with the experimental value and analytical models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".