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Record W3184008549 · doi:10.1088/2631-8695/ac1848

A numerical study of the effect of the thickness parameter on machining distortion for aluminum alloy plates

2021· article· en· W3184008549 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

VenueEngineering Research Express · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsDistortion (music)MachiningResidual stressMaterials scienceFinite element methodStructural engineeringDeformation (meteorology)AluminiumPosition (finance)Composite materialEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract The deformation produced after the machining of a structural component is known as part distortion. This phenomenon is a consequence of the inherent residual stresses that exist in raw materials. In this study, such phenomenon is numerically investigated in simple plate elements by considering their thicknesses and their corresponding contribution to part distortion. A total number of eleven flat plates were analyzed using a numerical part distortion procedure for finite element models that also considered their machining positions. The results of this study show that part distortion has more impact on slender plates because these present higher loads than thicker plates in which the residual stresses self-balance throughout their section. Consequently, the part distortion phenomena in simple structural flat plates are related the plate thickness, their machining position, and geometrical parameters.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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