Low‐Roughness 3D‐Printed Surfaces by Ironing for the Integration with Printed Electronics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The roughness of 3D‐printed surfaces poses a challenge when integrating fused filament fabrication (FFF) printing with printed electronics, leading to inconsistencies and breaks in the circuit traces. To improve the surface roughness, an ironing toolpath is proposed. The ironing toolpath involves the hot nozzle going over the printed surface with finer line spacing, remelting the surface to fill gaps, and creating a smooth finish. For further optimization, various ironing parameters are investigated including flow, speed, line spacing, and temperature. A wide range of materials is tested, including commonly used low‐temperature filaments (polylactic acid, polyethylene terephthalate, acrylonitrile butadiene styrene) and high‐temperature filaments (polysulfone, polyetherimide, polyether ether ketone) suitable for integration with printed electronics and medical applications. To collect the extensive datasets, an automated measurement system is deployed. With this method, surface roughness reductions of up to 96.6% are achieved and significant trends are identified. Lastly, the integration of 3D printing with electronics is demonstrated by printing a high‐resolution strain gauge structure on top of an ironed surface and embedding it into fully printed tweezers which can be used in medical robotics. The insights on ironing extend beyond electronics and can also be valuable in other areas where low surface roughness of FFF‐printed parts is required.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it