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Record W2761970609 · doi:10.15866/ireche.v5i3.6928

Effect of Foaming Time and Temperature on the Hardness of Al-Si-Cu-Mg Alloy Foam Cell Walls

2013· article· en· W2761970609 on OpenAlexaff
Anwarul Hasan, Amkee Kim

Bibliographic record

VenueInternational Review of Chemical Engineering (IRECHE) · 2013
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNanoindentationMaterials scienceAlloyEutectic systemIndentationVickers hardness testFoaming agentComposite materialIndentation hardnessMetal foamPhase (matter)MetallurgyMicrostructureAluminiumPorosityChemistry

Abstract

fetched live from OpenAlex

Al-Si-Cu-Mg alloy foams of different compositions and different cell morphologies were produced using the powder metallurgical method and by varying the foaming time and temperature during production. Hardness of the produced precursors and foams was measured using nanoindentation and micro indentation hardness measurement methods. Results obtained from both of the methods showed similar trend although the nanoindentation hardness of the specimens was consistently higher than the corresponding micro Vickers hardness. The precursor and foams obtained from Al-5wt.%Si-4wt.%Cu-4wt.%Mg (alloy 544) showed a higher hardness value than Al-3wt.%Si-2wt.%Cu-2wt.%Mg (alloy 322) precursor and foams made at the same foaming temperature and time because of their higher content of alloying elements. The hardness value of foam walls increased with the increase in foaming time at all foaming temperatures due to the increase of eutectic phase. Same density foams obtained from the same precursor but at different foaming temperatures were found to have different hardness values which indicate that local and global properties of foams with similar densities obtained from the same precursor will differ from each other if their foaming conditions are not the same

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)0.000
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 score0.053
Threshold uncertainty score0.644

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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Review of Chemical Engineering (IRECHE)Same topicAluminum Alloys Composites PropertiesFrench-language works237,207