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Record W1944725573 · doi:10.24908/pceea.v0i0.3882

DESIGN PARAMETERS AFFECTING TUMBLING MILL NATURAL FREQUENCIES

2011· article· en· W1944725573 on OpenAlexafffundvenue
Peter Radziszewski, Yu Quan, Julien Poirier

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMillGrindComminutionCorkMechanical engineeringBall millEngineeringNatural frequencyStiffnessMaterials scienceMetallurgyGrindingComposite materialVibrationStructural engineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

Tumbling mills describe a class of mechanical systems defined by a cylindrical chamber filled with balls and/or rock that rotates around its own longitudinal axis. This class of mechanical systems is used to grind to a desired quality different material in at least three industries: mining, cement and metal powders industries (ore, clinker and metal powder). These tumbling mills range in size from small 1 ft diameter lab mills to a 40 ft diameter semi-autogenous industrial mill and are composed of three main interactive and interdependent elements: the mill shell, liners/lifters and charge. These three elements work together to impact energy to the mill charge through the rotational motion of the mill shell. As with any mechanical system, the natural frequency of that system is essentially dictated by its mass and stiffness. In the case of a tumbling mill, a rotating system, the natural frequency of a given mill, as seen by a stationary observer, is also a function of the rotation speed. The aim of this paper The consequence for a rotating mill, and for that matter any rotating system, is that if the natural frequency of the mill should happen to match the operating rotating speed of the mill, resonance of the mill will occur. The objective of this paper is to explore the effect of mill diameter, length on mill natural frequencies as well as outline further work.

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)0.001
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 score0.112
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.189
Teacher spread0.172 · 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

Citations0
Published2011
Admission routes3
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicMineral Processing and GrindingFrench-language works237,207