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A Review of Backup Mechanism for Reducing Delamination when Drilling Composite Laminates

2016· review· en· W2555829298 on OpenAlexvenueno aff
C.C. Tsao, Hong Hocheng

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2016
Typereview
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
FundersNational Science Council
KeywordsDelamination (geology)Materials scienceComposite laminatesDrillComposite materialComposite numberDrillingBackupMechanism (biology)Fiber pull-outMechanical engineeringMetallurgyGeologyEngineering

Abstract

fetched live from OpenAlex

Over the past decades, composite materials have been increasingly utilized in various industries because of their superior mechanical properties and resistance to corrosion. Drilling is essential to produce precise holes when load-carrying structures are produced using composites. Because of the non-homogeneous and anisotropic property of composite laminates, delamination often occurs at the point where the drill exits, which affects reliability and safety. Some studies present a suppressed mechanism to prevent delamination when drilling composite laminates. The experimental results demonstrate delamination is significantly reduced by various suppressed mechanisms and greater feed rates produce the same level of delamination. The use of special drill geometries and backup has been demonstrated to be more advantageous than the use of adapted feed controls. The basis for the future development of a suppression mechanism for drilling composite laminates is determined.

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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.052
GPT teacher head0.418
Teacher spread0.366 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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