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Record W4409798064 · doi:10.1063/5.0245142

Optimizing aluminum content in DZ409 alloy: Analysis of microstructural and mechanical properties for high-temperature applications

2025· article· en· W4409798064 on OpenAlexfundno aff
Jiantao Wu, Bao-ping Wu, Pei Shi Sun

Bibliographic record

VenueAIP Advances · 2025
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsnot available
FundersUniversity of Guelph
KeywordsAlloyMaterials scienceAluminiumMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

This research investigates the microstructural characteristics of DZ409 alloys. It explores how varying aluminum (Al) contents by weight (3.42–4.22 wt. %) influenced carbide size, volume fraction, and phase distribution in both as-cast and heat-treated states. The results of this study revealed that the size of Metallic Carbide (MC) carbides exhibited a non-linear trend, initially increasing and subsequently decreasing with a higher Al content. Similar trends were observed in the size and area fraction of the γ/γ′ eutectic phase. Post-heat treatment analysis demonstrated that increased Al content significantly impacted interdendritic and dendritic stem phases, leading to augmented volume and size. The mechanical properties of the heat treatment associated with the DZ409 alloy were also analyzed, which showed some notable enhancement in the tensile strength and yield strength. The stress life analysis at 980 °C/MPa also indicates the increased Al content on alloy performance. The findings highlight the pivotal role of Al in terms of improved microstructural attributes and mechanical properties of DZ409 alloys. To conclude, optimizing the Al content can potentially improve the functionality and durability of DZ409 alloy, making them particularly suitable for high-temperature environments, such as gas turbine applications.

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 score0.014
Threshold uncertainty score0.402

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 head0.012
GPT teacher head0.230
Teacher spread0.217 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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