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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 OpenAlexaff
Yuhang Ding, Wanyuan Gui, Baoxian Nie, Wenya Bi, Cheng Zhong, Yanpeng Xue, Benli Luan

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2023
Admission routes1
Has abstractyes

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