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Record W4401871413 · doi:10.1016/j.heliyon.2024.e36925

Investigating the impact of drill material on hole quality in jute/palm fiber reinforced hybrid composite drilling with uncertainty analysis

2024· article· en· W4401871413 on OpenAlexafffund
Mohamed Slamani, Abdelmalek Elhadi, Salah Amroune, Mustapha Arslane, Walid Jomaa, Hassan Fouad, Jean-François Châtelain, Mohammad Jawaid

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsPolytechnique MontréalÉcole de Technologie Supérieure
FundersKing Saud UniversityUnited Arab Emirates UniversityMitacsMcGill University
KeywordsDrillDelamination (geology)DrillingCarbideMaterials scienceHigh-speed steelComposite materialComposite numberMetallurgyGeology

Abstract

fetched live from OpenAlex

This study presents a method for modelling, predicting, and evaluating the impact of drill materials on the drilling process of hybrid palm/jute polyester composites, with the aim of enhancing hole quality regarding delamination, circularity, and cylindricity. Three drill materials, including High-Speed Steel (HSS), 5 % Cobalt-coated High-Speed Steel (HSS-Co5), and Solid Carbide drills were tested, and their impacts on drilling performance were assessed. Through thorough experimentation and statistical analysis, significant differences in results were observed between HSS drills and both HSS-Co5 and Solid Carbide drills. However, the variation in results between HSS-Co5 and Solid Carbide drill results was minimal. Additionally, the findings highlight notable disparities among drill types concerning uncertainty. The results also indicate that feed rate, drill material, and their interaction play crucial roles in determining drilling efficiency. Specifically, HSS drills consistently outperformed HSS-Co5 and Solid carbide drills, demonstrating superior performance in minimizing delamination, improving circularity, and enhancing cylindricity along with lower uncertainty.

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 score0.019
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.280
Teacher spread0.269 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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