Acoustic emission monitoring of interlaminar delamination onset in carbon fibre composites
Bibliographic record
Abstract
This article presents the development of an experimental methodology based on acoustic emission wave detection for determining delamination onset and propagation in carbon fibre composite materials under quasi-static and fatigue loading. Delamination was investigated in quasi-static interlaminar fracture testing over a wide range of mixed-mode ratios ( G II / G T = 0, 0.3, 0.5 and 1) for unidirectional and woven samples. An acoustic emission wave detection method was developed to detect delamination onset, and the corresponding fracture toughness was computed. Interlaminar fracture toughness was also calculated by beam theory and from finite element analysis with the virtual crack closure technique. The mechanical testing results, acoustic emission monitoring and numerical model’s interlaminar fracture toughness were used to define delamination initiation criteria by drawing two-dimensional envelopes corresponding to G C = f( G II / G T ). The acoustic emission wave detection method showed damage accumulation before observable crack propagation, and its failure envelope corresponded to lower fracture energies than the standard test and modelling methods. Mode I fatigue testing with acoustic emission monitoring was performed on the woven samples for different energy release rate ratios ( G IMAX / G IC = 0.3–0.8). A first series of samples were tested to construct an onset delamination fatigue curve Δ G = f( N). A second series of samples were used to study the cumulative acoustic emission energy distribution during delamination growth. An unsupervised pattern recognition methodology is presented for crack opening and closing testing, in order to discriminate between fatigue signal noise and acoustic emission signals emitted from crack initiation and crack growth. Correlations were observed between the acoustic emission energy distribution, the load range, the delamination length and the crack growth rate.
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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 | 0.000 | 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".