Experimental and Computational Analysis of Low-Velocity Impact on Carbon-, Glass- and Mixed-Fiber Composite Plates
Bibliographic record
Abstract
One of the problems with composites is their weak impact damage resistance and post-impact mechanical properties. Composites are prone to delamination damage when impacted by low-speed projectiles because of the weak through-thickness strength. To combat the problem of delamination damage, composite parts are often over-designed with extra layers. However, this increases the cost, weight, and volume of the composite and, in some cases, may only provide moderate improvements to impact damage resistance. The selection of the optimal parameters for composite plates that give high impact resistance under low-velocity impact loads should consider several factors related to the properties of the materials as well as to how the composite product is manufactured. To obtain the desired impact resistance, it is essential to know the interrelationships between these parameters and the energy absorbed by the composite. Knowing which parameters affect the improvement of the composite impact resistance and which parameters give the most significant effect are the main issues in the composite industry. In this work, the impact response of composite laminates with various stacking sequences and resins was studied with the Instron 9250G drop-tower to determine the energy absorption. Three types of composites were used: carbon-fiber, glass-fiber, and mixed-fiber composite laminates. Also, these composites were characterized by different stacking sequences and resin types. The effect of several composite structural parameters on the absorbed energy of composite plates is studied. A finite element model was then used to find an optimized design with improved impact resistance based on the best attributes found from the experimental testing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".