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Record W2800911851 · doi:10.1139/tcsme-2016-0060

PULSED ELECTROCHEMICAL MICRO MACHINING OF INVAR (FE-NI) FILM USING AN ELECTRODE ARRAY

2016· article· en· W2800911851 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInvarElectrochemical machiningMaterials scienceMachiningElectrodeEtching (microfabrication)OptoelectronicsShadow maskComposite materialMetallurgyThermal expansionElectrolyteOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

Recently, invar (Fe-Ni) material has been applied to OLED shadow masks due to its thermal change characteristics and thermal expansion coefficient. The most widely used manufacturing methods for invar are etching and laser machining, but they have problems like non-machined areas generated by etching and surface burning in laser machining. For this reason, an alternative machining method is necessary. In this study, pulsed electrochemical machining (PECM) has been applied to fabricate an OLED shadow mask. PECM is a highly promising technology for shadow mask manufacturing because it can produce micro-scale and complex tapered holes in one process. A pilot experiment was carried out to find a suitable electrolyte for invar film, and an array of coated Ti electrodes was used to fabricate micro holes.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.556

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 head0.211
Teacher spread0.202 · 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