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Tribological behaviour of plasma nitrided cast iron D6510 and cast steel S0050A under the inclined-impact sliding condition with extremely high contact pressure

2017· article· en· W2620910739 on OpenAlexaff
Chen Zhao, J. Zhang, Xueyuan Nie

Bibliographic record

VenueJournal of Physics Conference Series · 2017
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceCast ironNitridingTribologyMetallurgyHardnessIndentation hardnessCrackingLayer (electronics)Composite materialSurface layerDiffusionMicrostructure

Abstract

fetched live from OpenAlex

Plasma nitriding as a surface modification was applied on two substrate materials: cast iron D6510 and cast steel S0050A. After measurement of the friction coefficients of the treated samples using a pin-on-disc tribotester, an inclined impact-sliding wear tester was utilized to investigate their tribological behaviour under tilting contact with extremely high contact pressure. While numerous surface fatigue cracks, severe chipping, and peeling of the compound layer were observed for the treated cast steel sample, the treated cast iron sample had far fewer surface fatigue cracks without chipping or peeling of the compound at the same test condition. The governing mechanisms of the treated cast iron sample's superior resistance to surface fatigue failure were revealed by studying the cross-sectional hardness and nitrogen concentration profile. Energy-dispersive X-ray spectroscopy (EDS) analysis indicated that the treated cast iron sample had a smaller nitrogen concentration gradient, which led to a smaller hardness gradient as measured. The results suggest that a smaller hardness gradient between the compound layer and the diffusion zone and a thicker hardened case was able to improve the wear resistance and surface fatigue cracking resistance against high contact loads. Moreover, the smaller friction coefficient of the treated cast iron sample could also be beneficial for improving the wear resistance.

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 score0.196
Threshold uncertainty score0.501

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 communication0.0000.001
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 head0.034
GPT teacher head0.254
Teacher spread0.220 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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