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Study on the Dimensional Changes and Residual Stresses in Carbonitrided and Ferritic Nitrocarburized SAE 1010 Plain Carbon Steel

2010· article· en· W2000644126 on OpenAlexaff
Chun Yan Nan, Derek O. Northwood, Randy J. Bowers, X. Sun

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsChrysler (Canada)University of Windsor
Fundersnot available
KeywordsCarbonitridingMaterials scienceResidual stressMetallurgyHardnessCarbon fibersCarbon steelComposite materialCorrosionComposite number

Abstract

fetched live from OpenAlex

Carbonitriding is a metallurgical surface modification technique that is widely used in the automotive industry to increase surface hardness and wear resistance. Given the problems associated with carbonitriding, such as dimensional distortion, oxidation and non-uniform surface hardness, nitrocarburizing has been proposed as an alternative heat treatment method to improve the surface characteristics. The major advantages of ferritic nitrocarburizing are the minimal dimensional changes and distortion due to the low process temperature at which no phase transformations occur. This increases productivity and product quality, and decreases costs. The focus of this study was to determine the effects of carbonitriding and ferritic nitrocarburizing processes on the dimensional changes and residual stresses in a steel used for automotive applications. Navy C-ring specimens and prototype stamped parts made from SAE 1010 plain carbon steel were used in the testing. Gas, vacuum and ion ferritic nitrocarburizing processes with different heat treatment parameters were investigated. X-ray diffraction techniques were used for the residual stresses evaluation and surface phase analysis of the specimens.

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)0.001
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 score0.028
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.222
Teacher spread0.208 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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