Mechanical properties of short‐fibre layered composites: prediction and experiment
Bibliographic record
Abstract
As a way of enhancing the mechanical properties of photopolymer‐based parts produced by layered manufacturing (LM) techniques, the use of short glass‐fibre reinforcements has been recently explored in the literature. This paper proposes a novel methodology that utilizes a modified rule‐of‐mixtures model for the prediction of the mechanical properties of such layered composites. The prediction process employs empirical data on (i) the fibre‐matrix interface, (ii) the fibres’ geometrical arrangement within the specimens (i.e. fibre‐orientation distribution), and (iii) the fibre‐length distribution. The effects of the fibre‐orientation and fibre‐length distributions are accounted for in the prediction model by the fibre‐length‐correction and orientation‐efficiency factors. Comparison of extensive experimental results and model‐based predictions of mechanical properties of layered composites demonstrated the effectiveness of the proposed estimation methodology.
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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 | 0.001 | 0.001 |
| 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".