Dynamic Elastic Modulus Variability in Anisotropic and Isotropic Materials: Comparison by Acoustic Emission
Bibliographic record
Abstract
This study compared the variation of the dynamic elastic modulus (E) of three types of composite pipes made by the filament winding process and a steel alloy specimen, according to signal source changes. The specimens were produced with three different winding angles, i.e., ±50°, ±52.5°, and ±55°. The moduli were obtained through a known signal source and the angular variation, according to two sensors positioned over the specimen's surface. In a previous article, the variation in the velocity of acoustic emission (AE) signals, performed in the same type of pipes, was discussed based on the standards for glass fiber-reinforced epoxy (GFRE) filament wound specimens. This work took these preliminary findings to compare with the results found for steel alloy pipes (SAE 1020). This data was used with appropriate equations to determine the dynamic elastic moduli of each material. It was found that, even for small angular differences, the modulus changes position concerning the lamination angle. Thus, the lower the quality control, the lower the final product with composite materials. As expected, for isotropic materials such as steel alloys, the modulus remains constant along the angles, while for anisotropic ones, it is dependent on the principal directions of stress and strain, or on the other hand, dependent on the correlation between the angular wave velocity of the AE signals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".