MétaCan
Menu
Back to cohort
Record W1974804003 · doi:10.1115/1.2744429

Effects of Molybdenum Content and Heat Treatment on Mechanical and Tribological Properties of a Low-Carbon Stellite® Alloy

2006· article· en· W1974804003 on OpenAlexafffund
Ping Huang, Rong Liu, Xijia Wu, Matthew Yao

Bibliographic record

VenueJournal of Engineering Materials and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsNational Research Council CanadaCarleton University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsStelliteMaterials scienceAlloyMicrostructureTribometerMetallurgyMolybdenumTribologyScanning electron microscopeComposite materialAbrasion (mechanical)

Abstract

fetched live from OpenAlex

The chemical composition of Stellite® 21 alloy was modified by doubling the molybdenum (Mo) content for enhanced corrosion and wear resistance. The specimens were fabricated using a casting technique. Half of the specimens experienced a heat treatment at 1050°C for an hour. The microstructure and phase analyses of the specimens were conducted using electron scanning microscopy and X-ray diffraction. The mechanical properties of the specimens were determined in terms of the ASTM Standard Test Method for Tension Testing of Metallic Materials (E8-96). The mechanical behaviors of individual phases in the specimen materials were investigated using a nano-indentation technique. The wear resistance of the specimens was evaluated on a ball-on-disk tribometer. The experimental results revealed that the increased Mo content had significant effects on the mechanical and tribological properties of the low-carbon Stellite® alloy and the heat treatment also influenced these properties.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.164
Teacher spread0.154 · 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

Citations43
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Engineering Materials and TechnologySame topicMetal and Thin Film MechanicsFrench-language works237,207