Investigation on progressive damage evolution for low-velocity impact simulation of woven composites
Bibliographic record
Abstract
• Models with empirical and physical-based damage evolutions were assessed. • The work spans a wide energy range covering cases with and without penetration. • Mechanical response and damage morphology were investigated with experiments. • Pros and cons for different damage evolutions for composites were highlighted. This research aims at comparing the capability of three damage models, the enhanced composite damage model (MAT055), the Pinho laminated fracture model (MAT261), and the composite softening deformation gradient decomposition (DGD) model (MAT299) for woven composite materials, in predicting damage from low-velocity impacts. The first of them considers the empirical damage evolution with residual strength softening factors, and the other two control the damage evolution with fracture mechanism. To assess their predictive capabilities regarding mechanical response and damage, low-velocity impact (LVI) response of aramid-fibre epoxy plain-woven composites at four energy levels, from 27.9 J to 109.5 J, was investigated. A finite element model with macro-homogeneous solid element formulation was developed, and a rigorous calibration of the various physical and non-physical parameters was conducted (for all material models). Low-velocity impact tests were performed to identify the different failure mechanisms, focusing on the penetration of the impactor into the woven composites. The MAT261 with linear damage evolution better fits the experimental data at high impact energy levels, where it demonstrates high accuracy on mechanical response and damage propagation area. However, it requires significantly longer computational time. Overall, this study provides an in-depth understanding of the limitations and advantages of those material models, providing insight into their suitability to simulate the impact behaviour of woven composites.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".