Strain and Damage Assessment of Treated and Untreated Luffa Mat Composite Using Acoustic Emission and Digital Image Correlation
Bibliographic record
Abstract
In this work, acoustic emission and digital image correlation were applied to three different composites reinforced with treated (2% and 5% NaOH) and untreated luffa fibers during tensile testing, to follow the evolution of the different damage modes and determine strains and Poisson’s ratio. The tensile test results showed that alkaline treatment of 5% improved Young’s modulus and tensile strength. In comparison, the 2% treatment showed the most outstanding improvements in mechanical properties. The K-means clustering methodology identified four types of damage: matrix cracking, fiber pull-out, delamination, and fiber breaking. The 5% treated composite had lower cumulative energy and hits than the untreated and 2% treated composites, implying that the T5% composite suffered less damage. The DIC results showed that the longitudinal strains found by the extensometer are very approximate to those found by DIC, this technique also allows us to find the transverse strains of the composites UT (0.324), T2% (0.295), and T5% (0.207%). It is shown that the 5% alkaline treatment leads to the decrease of Poisson’s ratio (0.2378) compared to 2% treated (0.3113) and untreated (0.3120) composites. Based on AE, DIC results, and mechanical properties, the T5% composite is the most successful.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".