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Record W2780699588 · doi:10.4236/jmmce.2018.61002

Effect of Atmospheric Plasma Paint Removal on the Fatigue Performance of 2024-T3 Aluminium Alloy Sheet

2017· article· en· W2780699588 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

VenueJournal of Minerals and Materials Characterization and Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAluminiumMetallurgyMaterials scienceAlloyAluminium alloy

Abstract

fetched live from OpenAlex

This article documents fatigue testing that was conducted using as-painted (baseline) and Atmospheric Plasma de-painted specimens made of 0.063 inch thick 2024-T3 aluminium alloy sheet. The intent of the test program was to determine whether AP de-painting would alter the fatigue properties of this aluminium substrate. AP de-painting process parameters were selected based on previous work that would remove the topcoat, while leaving most of the primer intact. This process was repeated five times to simulate service experience, where aircraft typically undergo five paint/de-paint cycles in their lifetime. As-painted (baseline) and five times de-painted specimens were fatigue tested under constant amplitude conditions, at two load ratios and several maximum stress levels. Ten samples per condition were used to establish statistical behaviour and repeatability. The test results and statistical analysis demonstrated that the selected AP process parameters did not have a detrimental effect on the fatigue performance of 2024-T3 aluminium alloy sheet.

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 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 score0.008
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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