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Record W2082944158 · doi:10.1108/17410380310698478

Intelligent process‐planning system or optimal CNC programming – a step towards complete automation of CNC programming

2003· article· en· W2082944158 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

VenueIntegrated Manufacturing Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNumerical controlAutomationProcess (computing)Automatic programmingFlexibility (engineering)ProgrammerComputer scienceEngineering drawingMachiningManufacturing engineeringPlan (archaeology)Blackboard (design pattern)EngineeringEmbedded systemProgramming languageMechanical engineering

Abstract

fetched live from OpenAlex

One of the bottle‐necks of computer numerical control (CNC) machining is the CNC programming. It relies on the experience and skills of the CNC programmer for the generation of the CNC program. The intelligent process‐planning system described in this paper generates a process plan automatically for CNC programming. It utilizes artificial intelligent technologies such as knowledge base, blackboard system and machine learning to extract machineable features, and proposes and selects optimal tools for the machining of the given part. Its flexibility and simplicity provide a convenient way to include new techniques and knowledge. The incorporation of this system with other CAD/CAM tools could effectively automate the CNC programming process.

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 categoriesMeta-epidemiology (narrow)
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 score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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