Development of SLA 3D printed volumes for leak testing of LHC Hi-Lumi cryomodules at STFC
Bibliographic record
Abstract
Daresbury Laboratory recently completed the build of a Radio Frequency Dipole (RFD) crab cavity cryomodule for the Super Proton Synchrotron (SPS). During the build the team faced challenges leak testing welds which could not be tested in the typical evacuation method. Each cryomodule build requires 34 unique cryogenic and insulation vacuum weld configurations, most of which are repeated across multiple weld sites. Each weld must be qualified inspected and tested (visual and leak) before the build can progress. A suite of bespoke 3D printed weld test tools and procedures have been developed with major savings to time and cost and improved quality of leak testing tooling, developing a methodology which can be adapted to many different weld configurations. All whilst maintaining a baseline leak rate of <5e-12 mbar L/s at or below 1e-3 mbar. The result was a repeatable and cost-effective means of performing high-accuracy leak tests in a short timescale. • STFC has built its first cryomodule for the CERN LHC – High luminosity project. • SLA 3D printed plastic volumes are a cost-effective material for leak detection. • Using Accura25, volumes have been printed that reach 1E-03 – 1E-04 mbar. • Accura25 vol have been used to leak test with <5E-12 mbar l/s accuracy. • SLA parts perform equally to stainless steel during helium leak testing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".