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Record W2799277207 · doi:10.1139/tcsme-2017-0045

Experimental investigation on hole quality in drilling of composite pipes

2018· article· en· W2799277207 on OpenAlexvenueno aff
Rajkumar Tibadia, Koustubh Patwardhan, Dhrumil Shah, Dinesh Shinde, Rakesh Chaudhari, Kanak Kalita

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDrillingComposite numberMaterials scienceAluminiumScanning electron microscopeQuality (philosophy)Process (computing)Central composite designComposite materialMechanical engineeringResponse surface methodologyEngineeringMetallurgyComputer sciencePhysics

Abstract

fetched live from OpenAlex

In recent years, the major reason for the rejection of composite pipes in industrial applications is due to the poor quality of the drilled hole. This paper investigates the effect of drilling process parameters on the hole quality in composite pipes made of an aluminium core surrounded by polyethylene layers. An empirical model is designed for the two input variables using response surface methodology (central composite design). An experimental investigation is carried out to study the effect of spindle speed and feed rate on quality of drilled holes, especially circularity error. It is observed that a moderate spindle speed and low feed rate are most effective in minimizing the circularity error. Microstructural investigation of drilled hole surface is also carried out using scanning electron microscopy (SEM).

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicAdvanced machining processes and optimizationFrench-language works237,207