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Record W2011059189 · doi:10.1115/detc2002/cie-34485

An Automated and Optimal Tool Path Planning System for the 3-1/2-1/2-Axis CNC Maching of Sculptured Parts

2002· article· en· W2011059189 on OpenAlexafffund
Zezhong C. Chen, Zuomin Dong, G. W. Vickers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsMachiningNumerical controlCutter locationVoronoi diagramTool pathTilt (camera)Machine toolEngineering drawingMotion planningMedial axisComputer scienceSurface (topology)Orientation (vector space)Mechanical engineeringEngineeringGeometryComputer visionMathematicsArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

Some sculptured parts with complex free-form surfaces usually require expensive 5-axis CNC machining. In this work, a cost-effective and practical solution to the 5-axis sculptured part machining – 3-1/2-1/2-axis CNC machining scheme – is discussed. An automatic and optimal tool path planning system for 3-1/2-1/2-axis CNC machining is introduced. The system uses fuzzy pattern recognition method and Voronoi diagram to subdivide a complex sculptured surface into an optimal number of uniform surface patches, finds the optimal cutter/part orientation for each surface patch, and plans 3-axis CNC tool paths for them. This type of machining is carried out by rotating the part to the cutter/part orientations discretely and sequentially using a tilt-rotary table attached to the 3-axis CNC machine. Under each orientation, the corresponding surface patch is machined using the 3-axis CNC tool paths. This tool path planning system can automatically generate efficient tool paths for sculptured parts and make the 3-1/2-1/2-axis CNC machining scheme as an applicable alternative of 5-axis CNC machining method.

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: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.366

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 head0.013
GPT teacher head0.259
Teacher spread0.247 · 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
Published2002
Admission routes2
Has abstractyes

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