Impact behavior of thin thermoplastic composites dependant on manufacturing parameters and layup
Bibliographic record
Abstract
The amount of structural application of carbon composites has grown massively in the last years. Carbon composites are known to provide superior performance with regard to their specific strength and stiffness. One of the major drawbacks, limiting the fields of application and defining knock down factors, is their sensitivity for impact damages. Thermoplastic resin systems are known to provide a better performance on impact behavior compared to thermoset resin systems. Within this paper, an investigation on the impact behavior of carbon composite plates with PEEK resin system is presented. The impact performance is derived by measurement of damage size, penetration depth and residual strength. For structural applications, composites provide the possibility to take use of tailored mechanical properties of laminates, like strength and stiffness by defining specific stacking sequences. Since the impact performance is also affected by the layup, different laminate configurations, starting from low in plane stiffness up to high stiffness, were investigated. Particularly for lightweight applications thin laminates are relevant. To provide the capability to determine the post impact compressive strength of thin plates, avoiding premature failure caused by buckling, a modified test bed for compression after impact test has been developed. Furthermore, within this work the influence of different manufacturing parameters on the impact performance and material properties was investigated, thus allowing an optimized manufacturing process with regard to cost and performance.
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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 | 0.000 |
| 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) | 0.002 | 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".