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Record W2950900031 · doi:10.3390/ma12111876

Investigation of the Quench Sensitivity of an AlSi10Mg Alloy in Permanent Mold and High-Pressure Vacuum Die Castings

2019· article· en· W2950900031 on OpenAlexafffund
Mengyun Liu, Zhan Zhang, Francis Breton, X.-Grant Chen

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsRio Tinto (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDie (integrated circuit)Materials scienceMoldMetallurgyAlloyDie castingSensitivity (control systems)Composite materialCastingEngineering

Abstract

fetched live from OpenAlex

The quench sensitivities of an AlSi10Mg alloy in permanent mold (PM) and high-pressure vacuum die (HPVD) castings were investigated with time–temperature–transformation and time–temperature–property diagrams using an interrupted quench technique. The quench-sensitive temperature range of the HPVD casting sample is 275–450 °C, and its nose temperature is 375 °C. The quench-sensitive range of the PM casting sample is 255–430 °C, and the nose temperature is 350 °C. The mechanical strength versus the cooling rate in both casting samples were predicted via a quench factor analysis and verified experimentally. The critical cooling rate of the HPVD casting sample is 20 °C/s whereas it is 17 °C/s for the PM casting sample. With a shorter critical time, higher nose temperature, and higher critical cooling rate, the HPVD casting sample exhibits a higher quench sensitivity than the PM casting sample. The differences in the quench sensitivities of the AlSi10Mg alloy due to the different casting processes is explained via the different precipitation behavior. At the nose temperature, coarse β-Mg2Si precipitates mainly precipitate along the grain boundaries in the HPVD casting sample, whereas rod-like β-Mg2Si precipitates distribute in the aluminum matrix in the PM 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.

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 score0.140
Threshold uncertainty score0.312

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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