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Record W2901696685 · doi:10.25071/10315/35396

Application Of Fiber Bragg Grating Sensor For Strain Measurement At The Notch Tip Under Cyclic Loading

2018· article· en· W2901696685 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

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFiber Bragg gratingMaterials sciencePHOSFOSStrain (injury)OpticsFiber optic sensorFiberOptoelectronicsGraded-index fiberComposite materialPhysics

Abstract

fetched live from OpenAlex

Notches are inevitable in many components and structures due to design limitations. In addition, they are the locations for stress concentration and are susceptible to fatigue failure. As a result, the cyclic stress/strain response at a notch is of key importance. Fiber Bragg Grating (FBG) sensors have been successfully utilized for mechanical and thermomechanical strain measurement in many cases; nevertheless, their capability of measuring strain at spots with intensive stress/strain has not yet been explored. In this research, FBG sensors are employed for strain measurement at the notch tip. A verification test was designed to substantiate the FBG measurements. The test involves a rectangular magnesium sheet with a center hole, subjected to uniaxial cyclic loading while the strain was measured at the notch tip using three different methods: strain gage, digital image correlation (DIC), and FBG. There were good agreements between the three measurements.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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