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Record W3182032621 · doi:10.29391/2021.100.021

Parametric Study of Automated Electrospark Deposition for Ni-Based Superalloys

2021· article· en· W3182032621 on OpenAlex
Pablo D. Enrique, Stephen Peterkin, Y. Zhou

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWelding Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Coatings
Canadian institutionsHuys Industries (Canada)University of Waterloo
Fundersnot available
KeywordsInconelSuperalloyHardfacingMaterials scienceCoatingDeposition (geology)MetallurgyCorrosionElectrodeSubstrate (aquarium)Composite materialWeldingMicrostructureAlloy

Abstract

fetched live from OpenAlex

Conventional electrospark deposition (ESD) processes used in industry are well suited to the coating and repair of small areas for the purpose of hardfacing, corrosion resistance, or dimensional restoration. Although significant advances have been made in the range of materials that can be processed, the comparatively slow deposition rate limits the potential applications of a traditional manually operated ESD process. In this study, an automated ESD system was demonstrated for the application of Ni-based superalloy (Inconel 718) coatings on Ni- and Fe-based substrates. A preliminary study was used to determine the influence of process parameters on an automated system, with capacitance, voltage, electrode force, and electrode travel speed parameters chosen to provide higher deposition rates while maintaining high deposition quality. A comparison of Inconel 718 and 316L stainless steel substrates found that the influence of substrate composition on coating hardness and coating composition was limited to the first 40um. These results pave the way for ESD of larger-area coatings and longer-duration repairs without the need for human operators.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.366

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 head0.013
GPT teacher head0.244
Teacher spread0.232 · 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