Comparison of two progressive damage models for predicting low-velocity impact behavior of woven composites
Bibliographic record
Abstract
This research focuses on comparing the two progressive damage models available in the explicit nonlinear finite element software LS-Dyna. To explore the prediction capabilities in terms of mechanical response and dominating failure modes in S2 glass woven composites, low velocity impact response at four different energies ranging from 27.9 J to 109.7 J were considered in this study. A macro-homogeneous solid element formulated finite element model was simulated to understand the response and failure mechanics in the laminate under low-velocity impact. The material modeling was carried out utilizing the MAT 55 and MAT 162 material models. An effort has been made for robust calibration of the various physical and non-physical parameters in both material cards for accurate predictions. The prediction capabilities of the models were then examined by comparing them against the experimental results, which fall within the deviation of ∼11%. The results show that MAT 162 yields a better resemblance with the damage morphology patterns and the delamination for the accounted impact zone, due to inclusion of strain-rate effect. Overall, this paper provides insight into the limitations and advantages of both material models, which establishes the route for the selection of the appropriate material model for simulating impact behavior in woven composites.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".