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Record W4244131196 · doi:10.1145/781650.781662

Cusp modeling for 5-axis surface machining

2003· article· en· W4244131196 on OpenAlexaff
Paul J. Gray, Fathy Ismail, Sanjeev Bedi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCusp (singularity)MachiningComputationSurface (topology)Computer scienceGraphicsTool pathComputer graphicsAlgebraic surfaceEngineering drawingGeometryComputer graphics (images)Algebraic numberMechanical engineeringAlgorithmMathematicsEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

Gouge checking and gouge measuring are essential parts of 5-axis tool-positioning strategies. To evaluate the quality of a tool path (i.e. the remaining material, commonly referred to as cusps), machining tests must usually be performed. If the tests prove to be unsatisfactory, new tool paths must be generated and run. In this paper, a new approach for modeling cusps is presented which makes use of recent advances in vector algebraic computation of swept volumes. The method uses the computer's graphics card to compute the cusp geometry from the swept volumes. The algorithm was verified with a machining test by measuring the cusp height between two adjacent passes with a Coordinate Measuring Machine and comparing it to the modeled cusp height.

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 score0.871
Threshold uncertainty score0.249

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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
Published2003
Admission routes1
Has abstractyes

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