MétaCan
Menu
Back to cohort

Three Point Bend Performance of Solutionized, Die Quenched and Heat Treated AA7075 Beam Members

2014· article· en· W1991160540 on OpenAlexaff
Alexander Bardelcik, Alexandre Bouhier, Michael J. Worswick

Bibliographic record

VenueMaterials science forum · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthFormabilityPrecipitation hardeningAlloyComposite materialElongationBeam (structure)AluminiumHardening (computing)MetallurgyStructural engineering

Abstract

fetched live from OpenAlex

To overcome the low room temperature formability of AA7075-T6 aluminum sheet, without sacrificing the high strength properties of this alloy, a hat section beam member was formed and quenched within a cold die immediately after a 20 minute solutionizing treatment. Natural aging for 24 hours followed the forming process which was then followed by various heat treatments that included a typical precipitation hardening (PH) and industrial paint bake (PB) temperature-time treatment. Tensile specimens were extracted from the beams to evaluate their mechanical properties. When compared to the as-received AA7075-T6 mechanical properties, the beams heat treated with the PH, PHPB and PB treatment resulted in a 5%, 13% and 20% reduction in ultimate tensile strength respectively. A similar trend was shown for the yield strength measurements. There was little effect of the heat treatments on the total elongation, with the PH condition showing a slight improvement. A backing plate was riveted to the beams and a quasi-static 3 point bend test was conducted to evaluate the crush performance. The peak load for the PH, PHPB and PB beams was 9.2, 8.5 and 7.3 kN respectively, but the calculated energy-displacement (or energy absorption) curves were similar for the PH and PHPB parts due to a more ductile fracture behavior for the PHPB material condition.

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 score0.013
Threshold uncertainty score0.557

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 studies0.0000.001
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 head0.006
GPT teacher head0.185
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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207