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Record W2996358649 · doi:10.1155/2019/8247914

On the Elevated Temperature, Tensile Properties of Al-Cu Cast Alloys: Role of Heat Treatment

2019· article· en· W2996358649 on OpenAlexaff
Abram Girgis, A. M. Samuel, H. W. Doty, S. Valtierra, F. H. Samuel

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceAlloyUltimate tensile strengthAluminiumBase (topology)MetallurgyComposite material

Abstract

fetched live from OpenAlex

The present study aims to investigate the mechanical properties of a newly developed aluminum Al-6.5% Cu-based alloy, coded HT200, as well as to determine how these properties can be further improved using grain refinement and heat treatment. As a result, the effects of different heat treatments and alloying additions on the ambient and high-temperature tensile properties were examined. Three alloys were selected for this study: (i) the base HT200 alloy (coded A), (ii) the base HT200 alloy containing 0.15% Ti + 0.15% Zr (coded B), and (iii) the base HT200 alloy containing 0.15% Ti + 0.15% Zr + 0.5%Ag (coded C). The properties of the three HT200 alloys were compared with those of 319 and 356 alloys (coded D and E, respectively), subjected to the same heat treatment conditions. The results obtained show the optimum high-temperature tensile properties and Q-values for the five alloys of interest, along with the corresponding heat treatment conditions associated with these properties. It was found that the T6 heat-treated alloy B was the optimum alloy in terms of properties obtained, with values comparable to those of commercial B319.0 and A356.0 alloys.

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 score0.067
Threshold uncertainty score0.380

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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