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Record W4210717938 · doi:10.3390/ma15030897

A Practical Comparison of Beam Shuttering Technologies for Pulsed Laser Micromachining Applications

2022· article· en· W4210717938 on OpenAlexafffund
Damon G. K. Aboud, Michael J. Wood, Gianluca Zeppetelli, Nithin Joy, Anne‐Marie Kietzig

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShutterSolenoidSurface micromachiningMaterials scienceLaserOpticsComputer scienceEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

In this report we investigate the performance of various beam shutter technologies when applied to femtosecond laser micromachining. Three different shutter options are considered: a mechanical blade shutter, a bistable rotary solenoid shutter, and an electro-optic modulator (EOM) shutter. We analyzed the behavior of each shutter type during repeated open/close commands (period of 10 ≤ T ≤ 200 ms) using both high-speed videography and practical micromachining experiments. To quantify the performance at varying cycle periods, we introduce a new variable called the compliance that characterizes the average state of the shutter with respect to its intended position. We found that the solenoid shutter responds poorly to sequential commands. The mechanical shutter provides reliable performance for cycled commands as short as T = 40 ms, but begins to lag significantly behind the control signal for T ≤ 20 ms. The EOM shutter provides the most precise and reliable performance, with an opening time of only 0.6 ms and a high compliance with the signal commands, even when cycled very quickly (T = 10 ms). Overall, this study acts as an extensive practical guide for other laser users when considering different shutter options for their laser system and desired application.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.515

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes2
Has abstractyes

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