Verification and Validation of Progressive Damage and Failure Analysis Methods for Intralaminar Failure Modes in Thermoplastic Composites
Bibliographic record
Abstract
Current progressive damage and failure analysis (PDFA) methods were developed around thermoset material systems and their physical behavior. However, the applicability of these analysis methods has not been evaluated for use in modeling thermoplastic material systems. As part of the Hi-Rate Composite Aircraft Manufacturing (HiCAM) Project sponsored by NASA, intralaminar failure characterization was completed on a thermoplastic material system, AS4D/PEKK-FC, with off-axis tension (OAT) and off-axis compression (OAC) testing. The experiments were able to capture matrix non-linear shear behavior and the failure envelope of the material system. Two PDFA methods, LS-DYNA MAT299 and NASA’s CompDam-DGD, were then employed to simulate individual coupon behavior and simulation results were compared to published verification and validation guidelines from the CMH-17 crashworthiness working group. The results found that the two modeling methods provided suitable representation of material behavior of thermoplastics.
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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 | 0.000 | 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 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".