Process modelling of discontinuous long fibre carbon/polyether ether ketone composites: Defect prediction
Bibliographic record
Abstract
A numerical model was developed to predict the defect formation during processing of compression moulded discontinuous long fibre carbon/polyether ether ketone composites. The model inputs are the material's temperature-dependant properties (through-thickness modulus and thermal shrinkage), the temperature distribution of the part during cooling and the applied moulding pressure. The material properties of carbon/polyether ether ketone prepreg were measured during cooling from melt using thermal analyses. The model was employed to identify regions on manufactured panels where pressure could be lost during cooling, which are prone to defect formation. Validation was performed by comparing the predicted defect areas against those found on flat panels moulded at pressures ranging from 10 to 110 bar. The model was then employed in a case study to show the importance of the cooling strategy in order to prevent defects on complex-shape components.
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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 | 0.000 |
| 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 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".