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Record W4229039827 · doi:10.21203/rs.3.rs-1601250/v1

Tool Positioning in Five Axis Machining on a Tensor Product Surface using Circular Rays

2022· preprint· en· W4229039827 on OpenAlexaff
Mukmeet Singh, Sanjeev Bedi, Stephen Mann

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsToroidSurface (topology)Tensor productMachiningTensor (intrinsic definition)Product (mathematics)Quadratic equationTranscendental equationGeometryMathematicsMathematical analysisPhysicsEngineeringMechanical engineeringNumerical analysisPure mathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, we give a method for positioning a toroidal tool on a tensor product surface with two points of contact. By firing vertical rays and circular rays from the tensor product surface to the toroidal cutting surface, we can find these two points of contact by solving only quadratic equations, whereas previous methods typically solve transcendental equations using iterative methods. The new method is faster than the methods presented in literature and is independent of the method used to define the part surface. In addition to general applicability, test results on three sample surfaces are presented.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.053
GPT teacher head0.385
Teacher spread0.331 · 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.

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
Published2022
Admission routes1
Has abstractyes

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