3D-printing magnetic susceptor filament for induction welding of thermoplastic composite sandwich panels
Bibliographic record
Abstract
A magnetic susceptor in a printable filament form is developed for the induction welding of thermoplastic composites. The susceptor is based on Ni particles embedded in a poly-ether-imide matrix. It is extruded and spooled to form a filament which can then be 3D-printed. The susceptor produces heat by hysteresis losses due to the magnetic properties of the Ni particles. As opposed to other typical electrically conductive heating elements, no percolation threshold needs to be achieved to produce heat as the Ni particles individually heat up when exposed to the induction coil's magnetic field. The heating efficiency of the susceptor filament and its deposition by the fused filament fabrication technique are demonstrated. The susceptor is used to assemble all thermoplastic composite sandwich panels. The sandwich samples are tested by the flatwise tensile test and a tensile strength of 4.6 MPa is obtained, which is equivalent to or higher than reported strengths for typical aerospace-grade sandwich panels. The printable susceptor opens the way to new induction welding or heating applications as it can be printed on a surface to produce a desired heating pattern. • A new 3D-printing magnetic susceptor filament for induction welding is manufactured. • The susceptor material is printed directly on a thermoplastic honeycomb core. • Skins are welded to the cores by induction using the printed susceptor and the samples are tested by FWT. • The feasibility of using this material for induction welding is demonstrated.
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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 | 0.001 |
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".