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Record W2991214518 · doi:10.1115/imece2019-11261

Computationally Efficient Thermo-Mechanical Analysis for Predicting Process-Induced Deformations of Composite Structures

2019· article· en· W2991214518 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

VenueVolume 2A: Advanced Manufacturing · 2019
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComposite numberKinematicsProcess (computing)Materials scienceConstitutive equationDisplacement fieldDeformation (meteorology)Field (mathematics)Computer scienceMechanical engineeringCuring (chemistry)Displacement (psychology)Structural engineeringComposite materialEngineeringFinite element methodMathematicsPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Abstract This paper presents an innovative numerical model for the calculation of process-induced deformations of composite structures. The capabilities of a refined one-dimensional model, based on the Carrera Unified Formulation, have been exploited to describe the complex displacement field that originates during the curing process of a composite component. The refined kinematic models adopted are able to describe a three-dimensional solution and make it possible to predict the through-thickness deformation that is one of the causes of the origins of the process-induced deformations. The evolution of the material properties during the curing process is evaluated using the software RAVEN and the manufacturing process is simulated using an ‘incrementally elastic’ constitutive model. The results demonstrate the capabilities of the present approach to predict the process-induced deformations including the complex stress field due to thermal and mechanical loads.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.417
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.239
Teacher spread0.232 · 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