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Record W4322772551 · doi:10.3390/ma16052047

A Review on Porosity Formation in Aluminum-Based Alloys

2023· review· en· W4322772551 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 · 2023
Typereview
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsPorosityMaterials scienceCastingRefining (metallurgy)AlloyAluminiumUltimate tensile strengthMetallurgyGrain sizeComposite materialSand castingMineralogyMoldGeology

Abstract

fetched live from OpenAlex

The main objective of this review is to analyze the equations proposed for expressing the effect of various parameters on porosity formation in aluminum-based alloys. These parameters include alloying elements, solidification rate, grain refining, modification, hydrogen content, as well as the applied pressure on porosity formation in such alloys. They are used to establish as precisely as possible a statistical model to describe the resulting porosity characteristics such as the percentage porosity and pore characteristics, as controlled by the chemical composition of the alloy, modification, grain refining, and the casting conditions. The measured parameters of percentage porosity, maximum pore area, average pore area, maximum pore length, and average pore length, which were obtained from statistical analysis, are discussed, and they are supported using optical micrographs, electron microscopic images of fractured tensile bars, as well as radiography. In addition, an analysis of the statistical data is presented. It should be noted that all alloys described were well degassed and filtered prior to casting.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.002

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 head0.057
GPT teacher head0.291
Teacher spread0.234 · 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