Novel Heating Elements for Induction Welding of Carbon Fiber/Polyphenylene Sulfide Thermoplastic Composites
Bibliographic record
Abstract
Conductive films of carbon nanofibers (CNFs) decorated/coated with metals, either silver (Ag) or nickel (Ni) are fabricated using a solution casting process and used as novel heating elements (HEs) for induction welding of carbon fiber/polyphenylene sulfide (CF/PPS) thermoplastic composites. Prior to making the films, the metal‐coated CNFs are prepared by an electroless plating method using Ag or Ni precursors. A solution of the metal‐coated CNFs is then casted onto a pure PPS film to give a robust conductive film upon solvent evaporation and annealing in an oven at 200 °C. SEM observation and electrical resistivity measurements reveal that the CNFs are successfully coated with the metals which result in a significant decrease of the films’ electrical resistivity. A third type of HE is also fabricated by solution mixing Ag‐coated CNFs and magnetic Fe3O4 nanoparticles. The welding efficiency of the fabricated films is assessed for induction welding of two different types of thermoplastic composites, that is, unidirectional pre‐impregnated 16 plies of CF/PPS compression‐molded in a quasi‐isotropic stacking sequence and 8‐ply of satin weave fabric CF/PPS compression‐molded in a cross‐ply stacking sequence. The mechanical apparent lap shear strength (LSS) of the induction‐welded joints is evaluated for the fabricated HEs and compared with the LSS of joints welded using conventional stainless steel mesh susceptors. Under similar testing conditions, Ag‐coated CNFs HEs lead to the highest LSS with an average value of ≈31.5 MPa. In general, the new HEs result in superior LSS and higher heating rates when compared to the metallic mesh counterparts. The present work offers a new perspective to push the boundaries toward high quality welding of thermoplastic composites using nanomaterials‐based HEs.
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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".