Characterization, analysis and prediction of damage onset in adhesively bonded joints using fracture mechanics and acoustic emission monitoring technique
Bibliographic record
Abstract
This study presents a new method for predicting the fatigue life of aluminum adhesive-bonded joints. The approach involves experimental tests to establish fatigue failure criteria using acoustic emission monitoring to detect damage onset, along with a finite element (FE) model to analyse changes in energy release rate at the embedded crack tip. The proposed method comprises three key steps. In the initial stage, a 3D failure surface criterion is experimentally generated, connecting the maximum total energy release rate (GT) and the mixed mode ratio (GII/GT) to the number of cycles (N) required for initiating the crack propagation. In the second step, the total energy release rate (GT) and the corresponding mixed mode ratio (GII/GT) at the crack tip of a single lap joint under different external loads are numerically determined utilizing the virtual crack closure technique. Mathematical equations linking the applied load (P) to the associated values of GTmax and GII/GT are established. Ultimately, once the energy release rate and mixed mode ratio for a given load are determined, the number of cycles required for initiation of crack growth can be extracted from the experimentally derived failure surface in the initial step. The predictive model shows excellent correlation with experimental data, depending solely on the adhesive system rather than joint design.
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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 | 0.001 | 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".