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Record W2755674660 · doi:10.5539/jmsr.v6n4p67

Study on the Quality of Quasi-Isotropic Composite Laminates Containing a Circular Hole

2017· article· en· W2755674660 on OpenAlexvenueno aff
Venkateswaran Santhanakrishnan Balakrishnan, Holger Seidlitz, Marcello Ambrosio, Tilo Schumann

Bibliographic record

VenueJournal of Materials Science Research · 2017
Typearticle
Languageen
FieldEngineering
TopicLaser and Thermal Forming Techniques
Canadian institutionsnot available
FundersBrandenburgische Technische Universität Cottbus-Senftenberg
KeywordsMaterials scienceComposite laminatesIsotropyDelamination (geology)Composite numberComposite materialOptics

Abstract

fetched live from OpenAlex

Composite structures used in modern engineering applications are often subjected to circular holes in order to join with metal components via riveting, bolting or pinning joints. These design based holes will interrupt the force flux in the direction of the fibers and create high stress concentrations near the notched area. Objective of the project is to understand the quality of the quasi-isotropic composite laminates ([45°, -45°, 0°, 90°]S) containing circular hole. To achieve this objective, a 3-phase portal milling machine and a 5kW continuous wave (cw) CO2 laser system were used to produce the circular holes in the composite laminates. The processing parameters for both the processes are varied to understand its influence. The quality of the circular hole produced by these methods are further investigated and compared in order to arrive at the optimum processing parameters for the given quasi-isotropic composite laminates.The hole qualities were evaluated by means of delamination factor caused by milling; cone angle, matrix evaporation for cw-CO2 laser system. For further comparisons, the optimal parameter combinations of both methods were selected for a tensile test according to the standard ASTM D5766-2002.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.158
GPT teacher head0.440
Teacher spread0.282 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2017
Admission routes1
Has abstractyes

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