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Record W2888603924 · doi:10.1177/0021998318796177

Characterization of aluminum/alumina/TiC hybrid composites in 3D produced by anodizing and accumulating roll bonding process using synchrotron radiation tomography

2018· article· en· W2888603924 on OpenAlexaffabout
K.M. Mostafijur Rahman, Jerzy A. Szpunar, Mohammad Reza Toroghinejad, George Belev

Bibliographic record

VenueJournal of Composite Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceComposite materialAccumulative roll bondingComposite numberSynchrotron radiationToughnessAluminiumHomogeneity (statistics)Optics

Abstract

fetched live from OpenAlex

Hybrid composites of Al/Al 2 O 3 /TiC were produced by anodizing and accumulative roll bonding processes. We implemented 3D imaging of the composites using synchrotron radiation tomography at Biomedical Imaging and Therapy’s 05B1-1 beamline at Canadian Light Source to collect information on internal structure of these hybrid composites i.e. distribution of particles and voids, particle/matrix interface and surface area distribution after different accumulative roll bonding passes. The volume and interface surface area distribution are responsible for strength and toughness of the composites along with other factors such as strain hardening and formation of ultrafine grains. We found that the mechanical properties improved as the number of accumulative roll bonding passes increases and the internal homogeneity of structure also improved. The composites after different accumulative roll bonding passes are studied where the number of reinforced particles and voids and their shape and size distribution were accurately being quantified in 3D to relate with mechanical properties of the composite. Such information should be of importance in analysis and improvement of the manufacturing process of these types of composites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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