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Record W4298129382 · doi:10.1117/12.2633367

Powerful standalone application for realistic optical tolerancing

2022· article· en· W4298129382 on OpenAlexaff
Nathalie Blanchard, Frédéric Lamontagne, Simon Paradis, Nichola Desnoyers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsZemaxTolerance analysisSoftwareComputer scienceProcess (computing)Lens (geology)Optical engineeringOptical proximity correctionEngineering drawingEngineeringOptics

Abstract

fetched live from OpenAlex

An innovative software application for a more realistic tolerance analysis has been developed recently by INO. The application is using optical and mechanical manufacturing databases as well as several equations to translate realistic manufacturing tolerances, optomechanical mounting interfaces, and centering methods into tilts and decenters perturbations, easily transferable to Zemax OpticStudio. The standalone application can be used by the optical designer to quickly verify the feasibility of a mounting and alignment technique according to the specific sensibilities of the current design. The optomechanical engineer can also easily validate or choose a better centering method as well as update the mechanical tolerance parameters. Once the parameter is fixed, the optical designer can export the new parameters into a Zemax OpticStudio file, updating Lens Data Editor and Tolerance Data Editor. Communication between both optical and optomechanical specialists is straightforward with this powerful tool, making the design process easier, quicker, and more accurate. This paper presents how INO is using its standalone application for tolerance analysis to overcome the complex simulation of various centering techniques. Through real examples, it will show how realistic tolerancing simulations impact the choice of appropriate centering method for the lens assembly.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.250
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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