MétaCan
Menu
Back to cohort
Record W2803778132 · doi:10.3390/coatings8050193

Effect of Annealing Treatment on Microstructure and Properties of Cr-Coatings Deposited on AISI 5140 Steel by Brush-Plating

2018· article· en· W2803778132 on OpenAlexafffund
Jianjun Hu, Jie Jiang, Hui Li, Xian Yang, Hongbin Xu, Yan Jin, Chaoping Ma, Qingshan Dong, Ning Guo

Bibliographic record

VenueCoatings · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsQueen's University
FundersProgram for Innovation Team Building at Institutions of Higher Education in ChongqingNatural Science Foundation of ChongqingChina Scholarship CouncilQueen's UniversityNational Natural Science Foundation of China
KeywordsBrushMicrostructureMetallurgyAnnealing (glass)Materials scienceComposite material

Abstract

fetched live from OpenAlex

Cr-coating was deposited on AISI 5140 steel by electro brush-plating, followed by annealing treatment at different temperatures, from 300 to 1100 °C. The microstructure evolution of the Cr-coating was characterized by backscattered electron imaging (BSEI) and energy dispersive spectrometry (EDS). The results show that the brush-plated sample has a nodular shaped microstructure, which is very stable at 300 °C of annealing. At 500 °C of annealing, the constitution of the microstructure changes from nodules to grains. As the annealing temperature further increases, the grains grow significantly. When the temperature reaches 1100 °C, a Cr-Fe solid-solution layer is formed within the original pure Cr-coatings. With increasing annealing temperature, the number of micro-cracks in the coating increases first and then decreases, reaching a maximum at 500 °C. The hardness and wear-resistance of the coating are improved when the annealing temperature increases to 1100 °C, owing to the decrease of micro-cracks that formed during brush-plating.

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 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 score0.008
Threshold uncertainty score0.709

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 teacher head, 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

Citations16
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCoatingsSame topicMetal and Thin Film MechanicsFrench-language works237,207