Prediction of Load-Bearing Capacity of Composite Parts with Low-Velocity Impact Damage: Identification of Intra- and Inter-Ply Constitutive Models
Bibliographic record
Abstract
Assessments of residual load-carrying capacity are often conducted for composite structural components that have received impact damage. The availability of a verified simulation methodology can provide significant cost savings when such assessments are required. To support the development of a reliable and accurate simulation methodology, this study investigated the predictive capabilities of a stacked solid-shell finite element model of a cylindrical composite component with a damage mechanics-based description of the intra-ply material response and a cohesive contact model used for simulation of the inter-ply behavior. Identification of material properties for the model was conducted through mechanical characterization. Special attention was paid to understanding the influence of non-physical parameters of the intra- and inter-ply material models on predicting compressive failure load of damaged composite cylinders. Calibration of the model conducted using the response surface methodology allowed for identifying rational values of the non-physical parameters. The results of simulations with the identified and calibrated finite element model showed reasonable correlation with experimental data in terms of the predicted failure loads and post-impact and post-failure damage modes. The investigated modeling technique can be recommended for evaluating the residual load-bearing capacity of flat and curved composite parts with impact damage working under the action of compressive loads.
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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) | 0.001 | 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".