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Record W4282052386 · doi:10.3390/ma15113965

Characterization of Si and SiO2 in Dust Emitted during Granite Polishing as a Function of Cutting Conditions

2022· article· en· W4282052386 on OpenAlexafffund
Jules Kouam, Victor Songméné, Ali Bahloul, A. M. Samuel

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsPolishingAbrasiveMaterials scienceChemical-mechanical planarizationMachiningParticle (ecology)Particle sizeScanning electron microscopeCharacterization (materials science)Shearing (physics)MetallurgyComposite materialNanotechnologyChemical engineeringGeology

Abstract

fetched live from OpenAlex

Particles emitted during manufacturing processes such as polishing can represent a serious danger for the environment and for occupational safety. The formation mechanisms responsible for these dust emissions include chip formation, friction at the tool/workpiece and chip/tool interfaces, shearing and cutting. These mechanisms thus depend on workpiece and tool properties, as well as the polishing conditions. In the case of granite polishing, particle emissions during polishing can contain chemical compounds such as silica, which represent harmful health risks for the worker. It is therefore important to characterize the particles emitted and to search for possible interactions between the particles (size and composition) and the machining conditions in order to find ways of reducing emissions at the source. In this study, an investigation was undertaken to characterize the particles emitted during granite polishing as a function of polishing conditions, type of granite, and abrasive grit sizes used. Scanning electron microscopy (SEM) was employed for particle morphology characterization and particle grain size and chemical composition were evaluated using X-ray diffraction (XRD) and energy dispersive X-ray (EDX) techniques, respectively. Results show that the influence of polishing speed and feed rate on particle emission depends mainly on the granite type used, providing useful information for controlling the polishing procedure, and thereby dust emission.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.926

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)0.0010.000

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 head0.007
GPT teacher head0.225
Teacher spread0.219 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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