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Record W3127739352 · doi:10.3390/met11020264

Effect of Thermal Debinding Conditions on the Sintered Density of Low-Pressure Powder Injection Molded Iron Parts

2021· article· en· W3127739352 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMetals · 2021
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsRio Tinto (Canada)École de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSinteringMaterials scienceMetal injection moldingRaw materialMoldMolding (decorative)CeramicViscosityComposite materialIron powderMetal powderMetalInjection mouldingMetallurgy

Abstract

fetched live from OpenAlex

Low-pressure powder injection molding (LPIM) is a cost-effective technology for producing intricate small metal parts at high, medium, and low production volumes in applications which, to date, have involved ceramics or spherical metal powders. Since the use of irregular metal powders represents a promising way to reduce overall production costs, this study aims to investigate the potential of manufacturing powder injection molded parts from irregular commercial iron powders using the LPIM approach. To this end, a low viscosity feedstock was injected into a rectangular mold cavity, thermally wick-debound using three different pre-sintering temperatures, and finally sintered using an identical sintering cycle. During debinding, an increase in pre-sintering temperature from 600 to 850 °C decreased the number of fine particles. This decreased the sintered density from 6.2 to 5.1 g/cm3, increased the average pore size from 9 to 14 μm, and decreased pore circularity from 67 to 59%.

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 score0.228
Threshold uncertainty score0.284

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 head0.232
Teacher spread0.219 · 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