Damage Characterization of Bio and Green Polyethylene–Birch Composites under Creep and Cyclic Testing with Multivariable Acoustic Emissions
Bibliographic record
Abstract
Despite the knowledge gained in recent years regarding the use of acoustic emissions (AEs) in ecologically friendly, natural fiber-reinforced composites (including certain composites with bio-sourced matrices), there is still a knowledge gap in the understanding of the difference in damage behavior between green and biocomposites. Thus, this article investigates the behavior of two comparable green and biocomposites with tests that better reflect real-life applications, i.e., load-unloading and creep testing, to determine the evolution of the damage process. Comparing the mechanical results with the AE, it can be concluded that the addition of a coupling agent (CA) markedly reduced the ratio of AE damage to mechanical damage. CA had an extremely beneficial effect on green composites because the Kaiser effect was dominant during cyclic testing. During the creep tests, the use of a CA also avoided the transition to new damaging phases in both composites. The long-term applications of PE green material must be chosen carefully because bio and green composites with similar properties exhibited different damage processes in tests such as cycling and creep that could not be previously understood using only monotonic testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".