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Record W2036302199 · doi:10.1002/pc.10211

Applications of fiber‐metal laminates

2000· article· en· W2036302199 on OpenAlexaffabout
Paul Straznicky, Jeremy Laliberté, C. Poon, A. Fahr

Bibliographic record

VenuePolymer Composites · 2000
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsMaterials scienceDurabilityAirframeComposite materialAerospaceCarbon fiber reinforced polymerFiberShotcreteComposite numberStructural engineeringAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Advanced composite materials and fiber‐metal laminates (FMLs) have the potential to offer significant improvements in weight savings and durability in airframe structures. FMLs are an advanced hybrid material system consisting of metal layers bonded with fiber‐reinforced polymer layers. This paper presents an overview of the history of fibre‐metal‐laminates, describes several common types and also discusses the results of impact durability experiments conducted at the Structures, Materials and Propulsion Laboratory of the Institute for Aerospace Research (SMPL‐IAR) of the National Research Council Canada (NRCC). An impact fixture was developed specifically for FMLs and is also described. Numerous low velocity impact tests have been carried out that demonstrate the improved impact response of FMLs over traditional composite materials. This research builds upon earlier impact testing on carbon‐fiber‐reinforced polymers conducted by NRCC and Carleton University.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations100
Published2000
Admission routes2
Has abstractyes

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