Vibration-assisted thermal bonding (VATB) of CF/PEEK thermoplastic composites: Influence of bonding parameters on the lap shear strength
Bibliographic record
Abstract
Thermoplastic composites excel in fatigue and impact resistance compared to thermoset composites. Notably, they allow in-situ consolidation using Automated Fiber Placement (AFP), which reduces manufacturing costs and energy consumption. However, AFP’s short processing time poses challenges in achieving optimal bond strength. To address this, the current research introduces an innovative technique called vibration-assisted thermal bonding (VATB). This method combines material preheating with sub-ultrasonic vibration during bonding to enable rapid, high-quality consolidation. The simultaneous application of heat and vibration raises the material temperature to its melting point and reduces polymer viscosity, respectively. To evaluate the effect of frequency-dependent vibratory pressure on consolidation quality, a custom machine integrating electrical heating and vibratory pressure was developed. The study systematically investigates the influence of bonding parameters such as preheating time, mold temperature, holding time, consolidation pressure, and vibration frequency. Lap shear strength is used as the key metric for evaluation. Results show VATB significantly improves bonding strength compared to conventional thermal bonding, with increases of up to 85 % at 355 ∘ C and 22 % at 375 ∘ C. These improvements are attributed to enhanced polymer chain penetration and shear-thinning effects, enabling stronger bonds at lower temperatures and shorter times, reducing post-treatment needs and costs. • Introduced a fast bonding method for thermoplastic composite materials. • Designed and manufactured a novel setup to combine heat and vibration precisely. • Leveraged shear-thinning property to improve polymer flow and enhance bonding strength. • Used vibration and heat to improve bonding quality at lower temperatures. • Reduced bonding time while increasing strength up to 85 % over standard methods.
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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".