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Record W3048239746 · doi:10.3390/ma13163534

On the Role of Hollow Aluminium Oxide Microballoons during Machining of AZ31 Magnesium Syntactic Foam

2020· article· en· W3048239746 on OpenAlexaff
Sathish Kannan, Salman Pervaiz, Abdulla Alhourani, R.J. Klassen, Rajiv Selvam, Meysam Haghshenas

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsWestern University
FundersAmerican University of Sharjah
KeywordsMaterials scienceGlass microsphereSyntactic foamComposite materialCeramicVolume fractionMachiningAluminiumCeramic foamDeformation (meteorology)MicrosphereMetallurgyChemical engineering

Abstract

fetched live from OpenAlex

The role played by hollow ceramic thin-walled aluminium oxide microballoons on the shear deformation characteristics of AZ31 Magnesium syntactic foam is studied through high-speed machining. The ceramic microballoons embedded in the AZ31 matrix provides the necessary stiffness for these novel foams. The effect of hollow ceramic microballoon properties, such as the volume fraction, thin wall thickness to diameter ratio, and microballoon diameter, profoundly affects the chip formation. A novel force model has been proposed to explain the causes of variation in cutting forces during chip formation. The results showed an increase in machining forces during cutting AZ31 foams dispersed with higher volume fraction and finer microballoons. At a lower (Davg/h) ratio, the mode of microballoon deformation was a combination of bubble burst and fracture through an effective load transfer mechanism with the plastic AZ31 Mg matrix. The developed force model explained the key role played by AZ31 matrix/alumina microballoon on tool surface friction and showed a better agreement with measured machining forces.

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 categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
Teacher spread0.188 · 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.

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

Citations20
Published2020
Admission routes1
Has abstractyes

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