Static, Quasi-Static and High Loading Rate Effects on Graphene Nano-Reinforced Adhesively Bonded Single-Lap Joints
Bibliographic record
Abstract
Crashworthiness, damage tolerance, energy absorption capability and safety are all important factors in the design of light-weight composite structures. Furthermore, in order to make such structures lighter and more resilient, and to avoid stress concentrations that can occur with mechanical fasteners such as bolted or welded joints, it is preferable to mate the structure’s various components with adhesively bonded joints. Therefore, a comprehensive understanding of the response of bonded joints subjected to loadings with various rates is of paramount importance in developing reliable structures. In this paper, the effects of high loading rates on the performance of nano-reinforced adhesively bonded single-lap joints with composite adherends are systematically investigated, and will be compared to the static and quasi-static results. Bonded joints mating carbon/epoxy and glass/epoxy adherends were subjected to tensile loadings under 1.5, and 3 mm/min, and very high loading rate of 2.04E+5 mm/min. The high loading rate tests were conducted using a modified instrumented pendulum, equipped with a specially designed impact load transfer apparatus. The results of the high load rate tests revealed the loading rate sensitivity of the adhesive/joints, as well as the positive influence of nano- reinforcement. In all, that overall stiffness and strength of the joints were increased with increasing loading rates and nano reinforcement. It was also recognized that the effect of nano reinforcement in few cases overcame the effect of loading rate, meaning that even small increases in the amount of nano-particles can overcome enormous increases in loading rates using the same epoxy resin base. The observed failure mechanisms were examined with a scanning electron microscope.
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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 | 0.000 | 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) | 0.001 | 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".