MétaCan
Menu
Back to cohort
Record W2810179225 · doi:10.1364/aio.2017.ath2a.1

High-speed in situ metrology for laser-based advanced manufacturing

2017· article· en· W2810179225 on OpenAlexaff
James M. Fräser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsAerospaceMetrologyAutomotive industryQuality assuranceProcess (computing)Focus (optics)LaserComputer scienceWeldingManufacturing engineeringLaser beamsMechanical engineeringProcess controlQuality (philosophy)EngineeringAerospace engineeringOpticsPhysics

Abstract

fetched live from OpenAlex

The world at the focus of an intense laser beam (kW or greater) can be a complicated place, but the quality of the cut, the stability of the weld or the final material properties of the additive manufactured part directly rely on this highly dynamic process. One approach would be to search the complex parameter space and the resulting part final characteristics. Instead we have developed inline coherent imaging to directly monitor the rapidly changing morphology during processing, to provide direct quality assurance at greater than 300kHz rates, and even closed-loop feedback to drive the process to the desired output. This technology, now commercialized, is been exploited in automotive and aerospace, with potential impact in healthcare.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Explore more

Same topicLaser Material Processing TechniquesFrench-language works237,207