Coefficient of thermal expansion of Nd-Fe-B magnetic particle polymer composites – experiments and stochastic finite element modeling
Bibliographic record
Abstract
Polymer composites containing magnetic fillers show great potential for various applications, including energy storage and medical devices. To aid in the engineering and design of these components, a thorough understanding of the thermal behavior of these inhomogeneous and often highly anisotropic materials is essential, particularly in terms of their coefficient of thermal expansion (CTE). To explore this, the authors produced magnetic composites using compression molding and casting techniques. The epoxy polymer matrix was modified with a commercial thickening agent, and isotropic magnetic particles were added as functional fillers. The microstructural morphology of the composites, including the distribution, dispersion, and alignment of the magnetic fillers, was analyzed through microscopy techniques like scanning electron microscopy. Furthermore, the glass transition temperature of both the polymer matrix and the composites was measured using differential scanning calorimetry (DSC). The CTEs of both the polymer matrix and the composites were experimentally determined using a custom-designed setup and analyzed through stochastic finite element analysis (SFEA). Five modeling scenarios were considered to predict the CTEs of the composite systems: fully random distribution, randomly aligned distribution, a ‘bonded’ interface contact, and a ‘no-separation’ interface contact for the in-plane directions of particles. For the out-of-plane direction, the randomly aligned distribution with ‘no-separation’ contact was also explored. Among the in-plane direction scenarios, the case with ‘bonded’ interface contact and randomly aligned distribution yielded the lowest CTE, while the case with fully random distribution and ‘no-separation’ interface contact resulted in the highest CTE. Finally, the experimental and SFEA modeling results were compared and discussed.
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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 | 0.001 |
| 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 | 0.001 | 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".