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Record W1970064874 · doi:10.1115/1.4005429

A New Mechanistic Approach for Micro End Milling Force Modeling

2012· article· en· W1970064874 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of CalgaryUniversity of Victoria
Fundersnot available
KeywordsRubbingRake angleEnhanced Data Rates for GSM EvolutionEnd millingRakeMaterials scienceShearing (physics)Root mean squareShear forceRADIUSContact forceBreakageMechanicsMechanical engineeringComposite materialComputer scienceEngineeringMachiningPhysicsMetallurgy

Abstract

fetched live from OpenAlex

This paper investigates the mechanistic modeling of micro end milling forces, with consideration of the effects of plowing, elastic recovery, effective rake angle, and flank face rubbing. Two different mechanistic models are developed for shearing- and plowing-dominant regimes. Micro end milling experiments are conducted to validate the model for Aluminum 6061; and, the model appropriately predicts force profiles for a wide range of feed rates, and prediction of the root mean square (RMS) values of the resultant forces is, on average, within a 12% error. The study of the model shows that plowing and rubbing force contributions are significant, especially at low feed rates. The edge radius is found to have a significant effect on plowing and rubbing force components and the effective rake angle, which indicates that it is important to maintain a low edge radius to reduce micro end milling forces.

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.

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)0.001
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 score0.550
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.216
Teacher spread0.204 · 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