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Record W3099267530 · doi:10.18280/acsm.440506

Study the Impact of Drilling Process Parameters on Natural Fiber Reinforced Herringbone Epoxy Composites

2020· article· en· W3099267530 on OpenAlexvenueno aff
M. Nagamadhu, Vijay Kumar Shankar, Samuel Sehgal, Rajath Upadhya

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDrillingDelamination (geology)Composite materialEpoxyThrustDrillComposite numberGrey relational analysisStructural engineeringMechanical engineeringEngineeringGeologyMetallurgy

Abstract

fetched live from OpenAlex

Common fiber composite overlays are attractive for a few applications, (for example, aviation and flying machine auxiliary parts) because of their predominant properties and bio-degradable. Typically, mechanical drilling process parameters are significant effect final machining process outcomes. It covers drilling performance like convectional drilling, grinding, vibration-influence twist drilling, and rapid boring, drilling apparatus geometry and materials, drilling actuated delamination and its smothering methodologies, thrust power, and wear rate. This paper aimed to address the effect and ranking of process parameters on trust force, torque, and delamination at both entry and exit of the hole sisal fabric herringbone reinforced epoxy composites. The herringbone woven sisal fabric reinforced epoxy composite is fabricated using conventional compression molding technique. The result shows that at higher speed and feed rate delamination at both entrance and exit, thrust force, and toque decreases. Optimum drill diameter found from 6 to 10 mm to obtain minimum responses. As four output parameters are deciding quality of drilled hole, further grey regression analysis is used to study the ranking of output responses. Form grey regression analysis it is found that for 2700 rpm spindle speed, 60 mm/min feed and 8 mm diameter exhibit minimum effort.

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 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)0.001
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 score0.160
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.053
GPT teacher head0.324
Teacher spread0.272 · 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 teacher head, 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicNatural Fiber Reinforced CompositesFrench-language works237,207