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Record W2426485740 · doi:10.1177/0021998316655393

Experimental and numerical investigation of the effect of gaps on fatigue behavior of unidirectional carbon/epoxy automated fiber placement laminates

2016· article· en· W2426485740 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 Composite Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialThermographyTension (geology)Fatigue testingStress (linguistics)Stress concentrationStructural engineeringFlexibility (engineering)InfraredFracture mechanicsUltimate tensile strength

Abstract

fetched live from OpenAlex

Automated fiber placement (AFP) process provides high potential to repeatability and flexibility required for manufacturing of complex parts in many industries. Performance of such parts can be influenced by AFP manufacturing induced defects such as gaps and overlaps. In this work, the effect of gaps on fatigue behavior of unidirectional carbon/epoxy laminates was investigated. Tension–tension fatigue tests were conducted on defected samples and compared to reference samples free from defects. Infrared thermography technique was used for monitoring of damage propagation during fatigue loading. Moreover, a fatigue progressive damage model (FPDM) was developed and applied to laminates containing gaps to predict fatigue damage progression and failure. The experimental results revealed that the effect of gaps depends on the maximum applied stress during fatigue. The higher is the applied stress, the higher is the reduction in fatigue life. Good agreement was found between the results of fatigue life prediction from the FPDM and the experimental results for defected specimens.

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 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 score0.037
Threshold uncertainty score0.364

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 head0.011
GPT teacher head0.251
Teacher spread0.241 · 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