Additive Manufactured Dielectrics for Aerospace Electrical Insulation Applications
Bibliographic record
Abstract
This paper investigates if there are inherent limitations with Additive Manufactured (AM) materials to preclude their use in electrical insulation applications, and if any anisotropic behavior exists with respect to print orientation. Two different AM processes, stereolithography (SLA) and selective laser sintering (SLS), were utilized to produce dielectric samples. Conductivity, Dielectric Relaxation Spectroscopy, bulk partial discharge, and dielectric breakdown tests were performed on samples printed in different orientations. Dielectric characterization was performed over a wide range of frequencies (DC to 1 MHz) and temperatures (-60C to +200C; material permitting). To help address the lack of meaningful electrical information in AM materials, this paper proposes a methodology for evaluation and dielectric quantification of basically "unknown" materials. Generally, the materials studied exhibited acceptable dielectric behavior but no clear advantages other than enabling complex multifunction designs (dielectric, structural and thermal). Dielectric strengths exceeding 30 kV/mm for a 1.0 mm thickness were exhibited in both SLA and SLS samples, with strength scaling laws explored over a range of thicknesses from 300 µm to 5.0 mm. For differing print orientations (XY-plane, YZ-pane, 30-degree inclined XY-plane) no discernible anisotropic dielectric behavior was observed. Motivations for the use of AM produced dielectrics in a civil aerospace context is also treated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".