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Record W2140057245 · doi:10.1109/coase.2010.5584195

Generative CAD and CAE integration using common data model

2010· article· en· W2140057245 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCADComputer scienceParametric modelData modelingProcess (computing)Associative propertySoftware engineeringParametric statisticsData miningData model (GIS)Engineering drawingEngineeringArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

The proposed method includes generation of a “common data model” (CDM) containing all the required parametric information for both CAD and CAE analysis. CDM stores and supplies the associative data to both CAD and CAE models and thus maintaining the associative dependencies between them. As the common data model gets modified according to designer's intent, the changes in it are consistently reflected in both CAD and CAE models through regeneration and analysis iteration. The same data model can be used to work with different CAD and CAE packages since it is totally independent from the software tools used. The data model is reusable and the whole process can be automated so that the embedded expertise in the cycles of the adaptive design and manufacturing can be consistently applied iteratively during product development processes.

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)0.000
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 score0.597
Threshold uncertainty score0.192

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 head0.042
GPT teacher head0.265
Teacher spread0.223 · 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

Quick stats

Citations17
Published2010
Admission routes1
Has abstractyes

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