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Record W3013782489 · doi:10.3390/met10040431

Are Large Particles of Iron Detrimental to Properties of Powder Metallurgy Steels?

2020· article· en· W3013782489 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

VenueMetals · 2020
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials sciencePowder metallurgyCompressibilityMartensiteComposite materialFabricationMetallurgyParticle-size distributionVolume fractionParticle sizeMicrostructureChemical engineeringThermodynamics

Abstract

fetched live from OpenAlex

It is experimentally shown that a removal of particles exceeding 100 microns in size from iron powders typically used in the fabrication of medium density powder metallurgy steels has a weak effect on apparent density, flowability and compressibility of blends as well as on density and strength of green bodies. An elimination of such particles, i.e., cutting off a heavy tail of a size distribution histogram at the 100 μm threshold, improves a compositional uniformity of sintered materials, but has no noticeable beneficial effect upon the strength of a final product, which is likely be determined by a fraction of pores and their shapes. A presence of soft pearlitic inclusions hardly matters unless their number density becomes so large that a 3D continuity (integrity) of a hard martensitic matrix is lost. This finding suggests that such an expensive preparatory step as sieving away large particles from as-received mixtures would bear no technological advantages. It was experimentally found that an attempt to lower the threshold below 100 μm noticeably worsened apparent density, flowability and compressibility.

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 score0.010
Threshold uncertainty score0.520

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 head0.042
GPT teacher head0.234
Teacher spread0.192 · 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