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Record W7027576060

Characterization of the formation of nickel-rich areas in PM steels and their effect on mechanical properties

2012· article· en· W7027576060 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNPARC · 2012
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSinteringMicrostructureNickelHomogeneity (statistics)Characterization (materials science)Diffusion
DOInot available

Abstract

fetched live from OpenAlex

Admixed chemical elements may lead to the formation of heterogeneous microstruc-tures in sintered steels. This is due primarily to low bulk-diffusion coefficients at conventional sintering tem peratures. For powder met allurgy (PM) steels, admixed nickel is well known to produce such heterogeneous microstructures where the element is not distributed evenly upon sintering, resulting in the formation of nickel-rich areas (NRAsj. In contrast, the principal mechanisms of nickel diffusion in PM steels are often misunderstood. This work presents a new approach to measure the bulk-diffusion coefficients of nickel in PM steels in order to determine the sintering conditions required to maximize its homogeneity in a steel matrix. Also, the influence of the local concentration of nickel on sintered microstructures is discussed. The characterization approach presented here could be used to characterize the diffusion of other alloying elements in PM steels, either admixed as elemental powders or introduced via master alloys.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.132

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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