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Cure Shrinkage Stress Analysis of Ultraviolet Curable Adhesive by Viscoelastic Analysis

2023· article· en· W4395067444 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

VenueKeisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu · 2023
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsShrinkageViscoelasticityAdhesiveMaterials scienceStress (linguistics)Composite materialUltraviolet

Abstract

fetched live from OpenAlex

A user subroutine has been ingeniously developed and integrated into the general FEM code ANSYS, dedicated to the evaluation of cure shrinkage stress in UV curable adhesives. Through experimentation, the user subroutine has exhibited exceptional accuracy in computing the relaxation behavior for varying degrees of cure. Moreover, an analysis employing dynamic mechanical analysis has convincingly demonstrated the ability of user subroutine to adeptly capture the intricate interplay between curing degree and frequency, manifesting as phase differences between displacement and stress. Finally, an analysis assuming curing shrinkage was performed. The results proved to be exceptionally encouraging, as the subroutine aptly reproduced the characteristic dependency of illuminance and film thickness on curing stress, which is notably distinctive to UV adhesives.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.182
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.019
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.258
Teacher spread0.250 · 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