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Record W4388705385 · doi:10.14447/jnmes.v26i4.a08

Effect of Ceramic Coated Tool on Stray Cut in Electrochemical Micromachining

2023· article· en· W4388705385 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

VenueJournal of New Materials for Electrochemical Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSurface micromachiningMaterials scienceCeramicBiomedical engineeringComposite materialEngineeringMedicineFabrication

Abstract

fetched live from OpenAlex

Electrochemical micromachining (ECMM) is an important machining process for machining materials susceptible to produce burrs during contact machining process.In this research copper plate of thickness 2mm and tool electrode of 464μm were machined through ECMM process under the sodium nitrate electrolyte (NaNO3).The tool electrode is coated with ceramic material and prevent stray current and chemical attack.The control factors namely, voltage, electrolyte concentration and duty cycle are varied on machining time and overcut.Higher voltage of 12V takes 1 minute to complete the through hole and the use of ceramic coated tool produces good arc on the circumference of the micro-hole and 65% reduction of overcut.The suitable range of electrolyte concentration for reduced machining time is 24 to 26g/lit.As per TOPSIS analysis, 10V, 24g/lit and 65% duty cycle and 11V, 24g/lit and 75% are the best optimal combinations.ANOVA results show that voltage and electrolyte concentration are the most significant factors with 57.73% and 28.83% contribution, respectively

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
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 score0.009
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.264
Teacher spread0.257 · 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