Prediction of <scp>quasi‐static</scp> elastic modulus for polyethylene‐terephthalate‐glycol prepared from fused deposition modeling
Bibliographic record
Abstract
Abstract This article is concerned about change of elastic modulus as a function of raster angle used in the fused deposition modeling and applicability of laminate theory to prediction of the elastic modulus. Polyethylene terephthalate glycol (PETG) was used for the experimental testing. In view that PETG shows viscous deformation which was not considered in the laminate theory, quasi‐static (QS) component of the elastic modulus was used to examine the applicability of laminate theory, which was achieved by extraction of the QS stress response to deformation through stress relaxation and then finite element modeling of the slope for the load–displacement curve before the relaxation. Two analytical approaches were used to examine the laminate theory. One was known as the mechanics of materials approach that considers matrix properties from the intralayer regions, and the other the elasticity model approach that also considers the interlayer regions. The study found that both approaches provided reasonable estimation of the QS elastic modulus but their values were slightly over the measured values. We provide evidence to suggest that the prediction accuracy could be improved by considering the change of interlayer filament contact with the change of raster angle.
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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".