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Record W2258115337 · doi:10.1139/tcsme-2005-0023

DEVELOPMENT OF AN EXPERIMENTAL TECHNIQUE TO MODEL DEFLECTIONS IN SURFACE GRINDING

2005· article· en· W2258115337 on OpenAlexaffvenueabout
Robert Bauer, Shangfei Lin, Andrew Warkentin

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGrindingMechanical engineeringNonlinear systemBacklashMaterials scienceMachine toolPower (physics)Surface integrityMechanicsStructural engineeringMachiningEngineeringPhysics

Abstract

fetched live from OpenAlex

Deflections in surface grinding can lead to geometrical inaccuracies in the components being ground and can also limit production rates in the grinding process. This paper develops and tests an experimental technique to indirectly measure the deflections in the surface grinding process by comparing the actual mass removed during a grinding pass to the mass that would have been removed if there were no deflections. The relationships between the spindle power, normal force and grinding deflections are then derived. The experimental technique was applied to both a Brown & Sharpe 824 Micromaster conventional surface grinding machine, as well as a Blohm Planomat 408 CNC creep-feed grinding machine in the Grinding Research Laboratory at Dalhousie University. For the Brown & Sharpe surface grinder, the experimental results show a linear relationship between the spindle power and the grinding deflections, which agree well with the literature. For the Blohm Planomat grinding machine, however, the relationship between the spindle power and the deflections is nonlinear. Further investigation revealed that this nonlinearity is primarily due to the presence of backlash in the spindle lead screw.

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: Methods · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score1.000

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 head0.014
GPT teacher head0.244
Teacher spread0.231 · 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
GenreMethods

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
Published2005
Admission routes3
Has abstractyes

Explore more

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