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Record W2086871935 · doi:10.2495/secm130141

The use of nitriding to enhance wear resistance of cast irons

2013· article· en· W2086871935 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

VenueWIT transactions on engineering sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNitridingMetallurgyMaterials scienceWear resistanceCast ironLayer (electronics)Composite material

Abstract

fetched live from OpenAlex

This research is focused on using nitriding to enhance the wear resistance of austempered ductile iron (ADI), ductile iron (DI), and gray iron (GI).Three gas nitriding processes, namely "Gas nitriding + nitrogen cooled down to 800 o F" (Blue), "Gas nitriding + cooled down to 300 o F" (Gray), and "Gas nitriding + oil quenched" (Oil) were used.This study was carried out through optical metallography, roughness measurements, microhardness, and SEM.The ball-ondisc wear tests were conducted under lubricated conditions.It was found that COF for all materials in all nitrided conditions was small (<0.045).The best wear performance was seen for ADI processed using the Gray and Oil gas nitriding processes.These processes produced a compound layer thickness of 4-6μm, a low surface roughness (0.8-1.3 μm, Ra) and a high surface microhardness (1800-2200 HV).The wear rate decreased with increasing surface microhardness and decreasing surface roughness.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.212
Teacher spread0.190 · 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