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Record W3002645942 · doi:10.1088/2053-1591/ab6ef0

Foaming of friction stir processed Al/MgCO <sub>3</sub> precursor via flame heating

2020· article· en· W3002645942 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

VenueMaterials Research Express · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceFriction stir processingFoaming agentMicrostructureAluminiumMetal foamAlloyComposite materialMagnesiumMetalMetallurgyPorosity

Abstract

fetched live from OpenAlex

Abstract In the recent years, metal foams have become promising candidate materials in the engineering sector owing to their light weight and excellent energy absorption properties. Friction stir processing (FSP) has emerged as a cost-effective route to fabricate metal foam precursors from bulk substrates. Although the short processing time in FSP is able to provide high productivity, the cost of the foaming agent, TiH 2 in the case of aluminum foams is still high. This paper introduces flame heating to achieve localized foaming of aluminum alloy AA5754 to explore the possibility of using magnesium carbonate as a foaming agent stirred using multi-pass FSP. A specially designed slot based strategy using two plates arranged in lap configuration is devised to stir the foaming agent and understand the material movement after each subsequent pass. Microscopy techniques were carried out to evaluate the distribution of the foaming agent after each pass and the resulting microstructure of the processed plates as well as the morphology of the foamed sample. EDX results showed higher Mg and O content around the pore walls.

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)0.001
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 score0.017
Threshold uncertainty score0.940

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 communication0.0000.001
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 head0.041
GPT teacher head0.273
Teacher spread0.232 · 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