Lightweighting large optomechanical structures in astronomy instrumentation utilising generative design and additive manufacturing
Bibliographic record
Abstract
Ground-based astronomical instruments have mass limits to ensure they can operate safely and accurately. Reducing the mass of optomechanical structures relieves mass budget for other components, improving the instrument’s performance. Many industries have adopted generative design (GD) and additive manufacturing (AM; 3D printing) to produce lightweight components. This is yet to be implemented in ground-based astronomical instrumentation; this paper aims to provide insight into the advantages and limitations of this approach. The project studied the Extremely Large Telescope (ELT) Mid-infrared Imager and Spectrograph (METIS) threemirror anastigmat (TMA); comparing the conventional, subtractive machined design with GD-AM designs. The TMA was selected due to its bespoke geometry constrained by an optical path, a conventional design which did not consider mass reduction, the size of the part (615mm × 530mm × 525mm) that necessitated a study of different AM methods, and the operational environment (70K & 10−6 Pa). The study created mass-optimised designs of the TMA using topology optimisation and field-driven design. The performance of these designs was analysed using finite element analysis and optical ray tracing. It was found that GD-AM designs pass the required optical, structural and modal requirements, with a greater than 30% weight reduction when compared to the conventional design. The study investigated wire arc additive manufacturing (WAAM), a viable method of manufacturing components of the TMA’s size. To commence the validation of WAAM for cryogenic environments, samples of WAAM aluminium 5356 were created and studied. The internal and external dimensions of two samples were investigated using X-ray computed tomography and the outgassing rate of two sets of three samples were evaluated to quantify the difference between machined and as-built samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".