Thermal Post-Processing of 3D Printed Polypropylene Parts for Vacuum Systems
Bibliographic record
Abstract
Access to vacuum systems is limited because of economic costs. A rapidly growing approach to reduce the costs of scientific equipment is to combine open-source hardware methods with digital distributed manufacturing with 3D printers. Although high-end 3D printers can manufacture vacuum components, again, the cost of access to tooling is economically prohibitive. Low-cost material extrusion 3D printing with plastic overcomes the cost issue, but two problems arise when attempting to use plastic in or as part of vacuum systems: the outgassing of polymers and their sealing. To overcome these challenges, this study explores the potential of using post-processing heat treatments to seal 3D printed polypropylene for use in vacuum environments. The effect of infill overlap and heat treatment with a readily available heat gun on 3D printed PP parts was investigated in detail on ISO-standardized KF vacuum fitting parts and with the use of computer vision-based monitoring of vacuum pump down velocities. The results showed that infill overlap and heat treatment both had a large impact on the vacuum pressures obtainable with 3D printed parts. Heat treatment combined with 98% infill reliably sealed parts for use in vacuum systems, which makes the use of low-cost desktop 3D printers viable for manufacturing vacuum components for open scientific hardware.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".