Damage identification of fiber-reinforced composites during three-pointbend tests based on acoustic emission and unsupervised learningmethods
Bibliographic record
Abstract
Advancements in composite materials design have rendered fiber-reinforced polymer composite (FRPC) materials an effective candidate for various engineering and industrial applications.A low specific mass and high specific mechanical stiffness and strength are attractive characteristics of FRPCs.However, studies to reliably identify mechanical failures in FRPCs is on ongoing endeavor.Therefore, in the present work, an acoustic emission (AE) technique combined with unsupervised learning methods was used to detect the damage mechanisms and progress in glass FRPC panels during three-point bend tests.The classification of the waveform for AE presented in this study was based on principal component analysis and the k-means method.The two most significant AE features were selected: peak frequency and amplitude.Frequency bands were obtained and compared to AE data from the technical literature associated with specific failure mechanisms, such as matrix cracking, fiber-matrix debonding, delamination, and fiber breakage.Amplitude values along with computed stress were analyzed as a function of time.
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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) | 0.000 | 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 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".