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Record W2938167652 · doi:10.5267/j.esm.2019.3.003

Electrolytic plasma polishing technique for improved surface finish of ED machined components

2019· article· en· W2938167652 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

VenueEngineering Solid Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsnot available
FundersMinistry of Education and Science of the Russian Federation
KeywordsPolishingMaterials scienceSurface finishSurface roughnessElectrolytePlasmaMetallurgyEngineering drawingComposite materialEngineeringElectrodePhysics

Abstract

fetched live from OpenAlex

The development of techniques that allow producing high-finish surfaces of geometricallycomplex parts of difficult-to-machine materials by electrical discharge machining (EDM) is an emerging research area. The aim of this work is to study the application of electrolytic-plasma polishing technique for the high-quality surface finish of the parts obtained by EDM process. The structural alloy steel 38KH2N2MA (GOST 4543 -71) was selected as the processing material. The morphology of the machined surface was examined using optical micrographs. It was observed that applying the electrolytic plasma polishing for the duration of 5 minutes results in reducing the surface roughness of the ED machined surface by a factor of 5. It is also concluded that the combined action of EDM with electrolytic plasma polishing method is suitable for attaining the desired surface finish.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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