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Record W2998355660 · doi:10.2351/1.5138184

Multi-dimensional control and optimization of ultrafast laser material processing

2017· article· en· W2998355660 on OpenAlexaff
Xiaoming Yu, Shuting Lei, Zenghu Chang, P. B. Corkum, Meng Zhang, Carlos Trallero–Herrero, David Grojo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsUltrashort pulseLaserMaterials scienceOpticsLaser beam machiningFemtosecondComputer scienceOptoelectronicsPhysicsLaser beams

Abstract

fetched live from OpenAlex

As an enabling manufacturing tool, ultrafast laser, with pulse duration in the range from tens of femtoseconds to tens of picoseconds, offers unparalleled performance in terms of resolution, repeatability, and 3D fabrication capability. Even though the laser-matter interaction time is short, the phenomena taking place during and after the energy deposition event are complex, dynamic, and extreme. Such complexity and dynamics, however, create a possibility to control and optimize ultrafast laser material processing from a multi-dimensional approach: Spatial beam shaping techniques can be used to sculpt laser focal shape in transverse and axial direction, creating optimal intensity distribution for a specific application; Temporal pulse shaping can modify the way laser energy is deposited to enhance energy coupling between laser beam and material; Laser wavelength can be tuned to match the type of material in use to introduce internal modification in an otherwise opaque material; And laser polarization can be used as another degree of freedom to tailor material response. This talk will present recent progress in the control and optimization of material processing with ultrashort pulse laser, for both subtractive (drilling, scribing) and additive (two-photon polymerization) manufacturing. This work extends the capability of ultrafast laser as a manufacturing tool, and puts forth a prospect of using laser for manufacturing purposes in a more effective and productive manner

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 score0.400
Threshold uncertainty score0.311

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 head0.009
GPT teacher head0.229
Teacher spread0.219 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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