Material Selection Methodology for an Induction Welding Magnetic Susceptor Based on Hysteresis Losses
Bibliographic record
Abstract
Induction welding is a fusion bonding process relying on the application of an alternating magnetic field to generate heat at the joining interface. Herein, magnetic hysteresis losses heating elements, called susceptors, which are made of magnetic particles dispersed in a thermoplastic polymer, are investigated. A methodology to identify the parameters influencing the heating rate of the susceptors and to select suitable magnetic particles for their fabrication is proposed. The applied magnetic field amplitude is modeled based on the induction coil geometry and the alternating electrical current introduced to it. Then, properties of the evaluated susceptor particles are obtained through measurements of their magnetic hysteresis. A case study is presented to validate the suitability of the proposed methodology. Particles of iron (Fe), nickel (Ni), and magnetite (Fe3O4) are evaluated as susceptor materials in polypropylene (PP) and polyetheretherketone (PEEK) matrices. Heating rates are predicted using the proposed method, and samples are produced and heated by induction to experimentally verify the results. Good agreement with the predictions is obtained. Ni is the most suitable susceptor material for a PP matrix, while Fe3O4 is preferable for PEEK.
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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 | 0.001 | 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".