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Record W1992253839 · doi:10.1504/ijmr.2010.029667

Data integration from product design to assembly planning in a collaborative environment

2009· article· en· W1992253839 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Manufacturing Research · 2009
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKey (lock)Systems engineeringProduct designInterface (matter)Downstream (manufacturing)Product (mathematics)Process (computing)Manufacturing engineeringNew product developmentProduct engineeringCADConcurrent engineeringComputer scienceEngineeringDesign review (U.S. government)Assembly modellingEngineering drawingProcess integrationProcess engineeringOperations managementOperating system

Abstract

fetched live from OpenAlex

Virtual Manufacturing (VM) evaluates products in Virtual Environments (VEs) before actual production. A key issue in VM systems is the integration of design information into different product development stages. A product design model has to be accessible and reusable by downstream applications, such as product process planning and assembly planning. However, existing product modelling systems cannot fully support the downstream applications. This paper presents a method of extracting product design information from its CAD models for assembly planning and tool accessibility analysis. A web-based interface is developed for the system implementation and the process simulation. [Received 1 December 2008; Revised 21 July 2009; Accepted 31 July 2009]

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.087
GPT teacher head0.361
Teacher spread0.274 · 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