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X-ray tomography analysis of intermetallics and voids in 6XXX alloys

2025· article· en· W4409009562 on OpenAlexaff
Tristan E. Kammbach, Elisa Cantergiani, Tillmann Robert Neu, Paul H. Kamm, Jonathan Friedli, Francisco García‐Moreno

Bibliographic record

VenueScripta Materialia · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsNovelis (Canada)
FundersPaul Scherrer Institut
KeywordsMaterials scienceIntermetallicTomographyMetallurgyRadiologyAlloyMedicine

Abstract

fetched live from OpenAlex

Sustainable aluminum production for the automotive industry with a high content of recycled material offers considerable potential for CO 2 footprint reduction and cost savings. A three-dimensional characterization of cast AA6016 alloys with <50 wt% and >75 wt% of recycled aluminum was carried out to assess the volume fraction, total number and surface area of intermetallics and casting voids using high-resolution synchrotron X-ray tomography. A new data analysis method is presented to describe morphology of intermetallics based on a surface area/volume by volume plot, which allows for better classification of their different geometry clusters even for small modifications of Fe and Mn in the cast alloy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2025
Admission routes1
Has abstractyes

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