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Record W4310147659 · doi:10.3390/applmech3040076

An Innovative Approach to Improving Residual Stress Distribution and Metallurgical Refinement of Forged AA7175 Applied in the Aeronautical Industry

2022· article· en· W4310147659 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

VenueApplied Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à RimouskiUniversité du Québec à Trois-RivièresUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsResidual stressShot peeningPeeningQuenching (fluorescence)Materials scienceProcess (computing)AlloyVibrationResidualDistribution (mathematics)Computer scienceMechanical engineeringManufacturing engineeringProcess engineeringMetallurgyAutomotive engineeringEngineering

Abstract

fetched live from OpenAlex

In this study, a novel approach for residual stress (RS) distribution on forged AA7175 is considered to replace and simplify the manufacturing process, based on the lean manufacturing concept. AA7175 alloy is a quench-sensitive material applied in the aeronautics industry, which is subjected to vibration and cyclic loads leading to fatigue failure. Generally, costly postprocessing operations, such as shot peening, are used to modify RS on the surfaces of parts. Considering the fact that this operation is usually performed manually and is costly, the industrial sectors have been searching for an alternative to simplify the process. Here, quenching and T74 aging are found to advantageously modify RS distribution by forming compressive RS on parts’ surface layers. The proposed heat treatment allows for the removal of the shot-peening process, helping to reduce the costs associated with the manufacturing process and to increase production quality.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.208
Teacher spread0.195 · 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