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Record W4363649859 · doi:10.1016/j.jmrt.2023.04.028

Elimination of elemental segregation by high-speed laser remelting for ultra-high-speed laser cladding Inconel 625 coatings

2023· article· en· W4363649859 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.

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

Bibliographic record

VenueJournal of Materials Research and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsWestern University
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceInconel 625InconelLaserMetallurgyCladding (metalworking)Laser power scalingSurface roughnessHigh-speed steelAlloyComposite materialOptics

Abstract

fetched live from OpenAlex

Ultra-high-speed laser cladding (UHSLC) is an effective way to deposit Inconel 625 coatings on 27SiMn steel to prolong its service life. However, elemental segregation in the Inconel 625 coatings is serious and high-speed laser remelting (HSLR) was investigated in order to tackle the issue. The HSLR treatment was also found to reduce the surface roughness of UHSLC Inconel 625 laser cladding significantly, while enhancing their wear resistance. Furthermore, finite element simulation suggests that a higher remelting power may result in a better melt pool fluidity and a more uniform composition distribution.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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