Characterization of low-velocity impact and post-impact damage of luffa mat composite using acoustic emission and digital image correlation
Bibliographic record
Abstract
In this paper, low-velocity impact and compression after impact damage tolerance of composite reinforced with natural luffa mat were studied for the first time. The effect of impact energy and the influence of the damaged area on the residual mechanical properties under compression were investigated. Acoustic emission (AE), digital image correlation (DIC) and scanning electron microscopy (SEM) were used for the evolution of different damage modes and displacement fields. The findings of the experiments reveal that compression after impact tests of 1, 2, and 3J show a significant effect of the residual damage which decreases residual compressive strength by 12.61, 24.14, and 30.9%, respectively, compared to the unimpacted composite, but Young’s modulus was not significantly affected. Multivariable statistical analysis of the AE signals identified four classes of damage: matrix cracking, fiber-matrix debonding, delamination, and fiber failure. It also showed that the damage mode of unimpacted composites which presents the majority of the amplitude events of the AE signals is mainly due to fiber failure, by contrast, for impacted composites the damage mode is mainly due to fiber-matrix debonding. The AE results are convincing and they were confirmed by SEM images of the fractured faces of the specimens, which revealed the main causes of material failure during the compression after impact test. The DIC system monitored the effect of pre-existing damage under compressive loading and found that increasing impact energy increases the stress concentration around the impacted area and has a significant effect on residual crack development, much more in the loading direction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".