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.
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 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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".