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Record W4319457633 · doi:10.1139/tcsme-2022-0150

The effect of piercing and drilling processes on burr formation and delamination of aged carbon and aramid fiber-reinforced polymer composites

2023· article· en· W4319457633 on OpenAlexvenueno aff
Kaan Emre Engin, Ali İhsan Kaya, Mahmut Tandogan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAramidFibre-reinforced plasticDelamination (geology)Materials scienceComposite materialDrillingMachiningFiberMetallurgy

Abstract

fetched live from OpenAlex

There are two major problems with fiber-reinforced polymer (FRP) composites during their machining that need to be addressed. The first concern is the delamination and formation of burrs at machined edges, and the second is the effects of aging leading to mechanical deterioration. In this study, carbon FRP (CFRP) and aramid FRP (AFRP) composites were manufactured by vacuum infusion method and aged for 2 years under natural environmental conditions. Piercing with three different clearances (1%, 5%, and 10% of sheet thickness) and speed of 4 m/s were performed. Additionally, conventional drilling was carried out at a feed rate of 0.2 m/min. The highest delamination factor difference between piercing and drilling processes was calculated as 7.3% and 13.9% for CFRP and AFRP, respectively. The highest burr amounts for AFRP and CFRP composites were obtained as 91.5% and 39% at 10% clearance for piercing process and 123% and 32.1% for drilling process, respectively. Compared with drilling, piercing generates less burr formation except for CFRP composites in the case of 10% clearance and more precise hole production. It is understood that piercing results significantly improve when smaller clearances up to 5% of the sheet thickness are utilized.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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