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A First-Order Cutting Process Ontology for Sheet Metal Parts

2009· book-chapter· en· W71520285 on OpenAlexaff
Arnaud Delaval

Bibliographic record

VenueFrontiers in artificial intelligence and applications · 2009
Typebook-chapter
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSheet metalProcess (computing)OntologyOrder (exchange)Computer scienceEngineering drawingManufacturing engineeringEngineeringMechanical engineeringBusinessPhilosophyEpistemologyProgramming language

Abstract

fetched live from OpenAlex

The semantic integration of manufacturing systems has been impeded by the lack of rigorous ontologies for specific domains of manufacturing processes and resources. In this paper we present a cutting process ontology for 2D shapes such as sheet metal parts, axiomatized in first-order logic. This ontology is an extension of the ontology of ISO 18629 (Process Specification Language) and an earlier shape ontology first used to support object recognition. The full ontology consists of an axiomatization of all possible ways to change a surface as the result of a cutting process and a taxonomy of cutting processes. All component ontologies are verified using representation theorems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.050
GPT teacher head0.300
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2009
Admission routes1
Has abstractyes

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