Experimental investigation on the low-velocity impact responses of hybrid CFRP–elastomer–metal laminates with various rubber layer positions and impact energies
Bibliographic record
Abstract
Hybrid carbon fiber reinforced plastics (CFRP)–elastomer–metal laminated plates (HyCEML) have drawn much attention for their improved impact resistance and high strength. This study investigates the influence of rubber layer placement strategies and impact energy on the low-velocity impact response of HyCEML. Drop-weight impact tests combined with three-dimensional digital image correlation (3D-DIC) technology analyze the failure modes, energy absorption efficiency, and out-of-plane deformation characteristics of different specimen configurations. For the impact tests on HyCEML with different specimen configurations and energy level of 30 J at room temperature, results indicate that under constant energy condition, the position of the rubber layer significantly affects impact performance. When the rubber layer is concentrated on the impact side, more complex failure mechanisms are induced, leading to the higher energy absorption efficiency of 97.9%. When the rubber layer is placed centrally within the laminate, the formation of plastic hinges effectively absorbs and dissipates impact energy, mitigating crack initiation, and propagation. This configuration exhibits a lower peak force of 3.56 kN, demonstrating superior impact resistance and toughness. In comparison, when the rubber layers are symmetrically distributed on both sides of the laminate, the energy absorption efficiency is reduced to 89.9%, and prominent metal cracking and fiber breakage occur in the impact region, leading to a less effective improvement in impact resistance. Additionally, the study reveals that as the impact energy increases, the impact response of HyCEML with a single configuration exhibits a monotonic trend. These findings provide valuable insights for understanding and predicting the mechanical behavior of HyCEML under varying impact energy levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".