Vibration-Assisted Thermal Repairing (VATR) of thermoplastic composites
Bibliographic record
Abstract
Repairing composite structures is crucial for extending their service life, and there is an increasing need for repair techniques compatible with thermoplastic composites (TPCs). As the significance of welding TPC joints grows, evaluating their repairability becomes essential. Therefore, this study focuses on developing an innovative method called vibration-assisted thermal repairing (VATR) for matrix repairing of carbon fiber/polyetheretherketone (CF/PEEK) thermoplastic joints by inserting an amorphous polyetherimide (PEI) resin at the interface of two CF/PEEK substrates. In this regard, initially, a four-layer CF/PEEK laminate was manufactured using Automated Fiber Placement (AFP). The CF/PEEK specimens were then stacked with an amorphous PEI layer inserted between them and welded at 310 °C under a constant pressure using two methods: traditional thermal repairing and vibration-assisted thermal repairing. To study the feasibility of VATR technique, a custom experimental setup was designed and built to enable controlled thermal welding, with and without the application of vibration. The effects of frequency and time on the lap shear strength and void content were then compared for both repair methods. Additionally, interface zone mapping was employed using a pseudo-coloring method to analyze the effect of vibration on polymer chain diffusion. Results from the VATR method revealed a stronger and more uniform repair interface, with greater diffusion of PEI in the parental CF/PEEK substrates compared to traditional thermal repairing. Overall, this new repair method demonstrated significant potential for TPC joint repair, showing a 22% improvement in shear strength and a 35% reduction in void content.
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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".